SaProt-1.3b was running on fabricated weights due to a config mismatch that strict=False happily hid—the checkpoint has 66 layers at 1280 hidden dim, not 40 layers at 2560—and now from_pretrained reads the real config and refuses to build on shape mismatches, bringing the model to near-parity (X_emb=0.99508, just shy of the 0.9987–0.9996 ESMC band due to bf16 accumulation over those 66 residual layers). Perf-gate false positives between machines of the same card type are fixed by adding machine-id (socket.gethostname()) under the card-type baseline block. The release adds multi-card data-parallel fanout for SaProt embeddings via --devices, seeds perf baselines for esmc-300m, esmc-6b, saprot-650m, and boltz2-affinity (activating their regression gates), hardens the Boltz-2 and Protenix-v2 flagship legs with 5+5 seeds and alignment-free metrics (TM-score, CA-lDDT), widens the Boltz-2 affinity leg to three targets with pose and pocket metrics, root-causes and fixes a FKBP12 affinity GAP from unseeded global RNG draws in the worker, and adds HSA (L585) as the first L300–800 pharma-realistic target—plus drops ProteinMPNN entirely (CPU-only, redundant with BoltzGen's inverse-fold path).

This release brings two new capabilities that expand tt-bio's toolkit for protein design workflows: SaProt structure-aware embeddings and ProteinMPNN inverse folding for fixed-backbone design. SaProt fuses amino-acid and Foldseek-3Di vocabularies into an ESM-2 encoder, letting you embed proteins with awareness of their 3D structure; ProteinMPNN then lets you generate sequences that fold back to a given backbone—the natural next step after running a structure predictor like Boltz-2 or bringing your own backbone. Both features are fully gated and validated on Blackhole P150a hardware with no performance regression across the existing model lineup (Boltz-2, ESMFold2, Protenix-v2, OpenDDE, ESMC), embedding parity tests confirm numerical accuracy, and ProteinMPNN runs on CPU via data-parallel fanout rather than on-device porting. See docs/proteinmpnn-port.md for details on the design choice.

This release brings antibody-antigen co-folding via OpenDDE (built on Protenix-v2 with a structural-token expander) alongside a fused-RoPE attention kernel that speeds up embedding without accuracy loss, plus optional diffusion trace replay that collapses per-step host dispatch overhead across Boltz-2, BoltzGen, Protenix-v2, and OpenDDE. The release also establishes perf and UX regression gates as standing test legs, fixing two release-blocking issues: OF3 pairformer gates now gracefully skip when missing host keys, and the perf gate now compares against hardware-specific baselines so P150a runs aren't falsely judged against P300c numbers. All shipped models pass gate thresholds with no performance regressions observed.

The release introduces HivemindSweeper, a new mode for system monitoring, alongside a shift to distributing packages via ppa.tenstorrent.com for easier installation.

The VS Code toolkit now includes a comprehensive guide to joyfully monkeypatching TT-NN without modifying your precious tt-metal checkouts.

Galaxy 6U systems get a safer stress-test exit: the tool now performs the KMD reset handshake when resetting Blackhole Galaxy hardware (#37), so the kernel driver and the burn-in workload agree about device state instead of leaving a rack-scale box in limbo after a run.

Dstack now introduces endpoint presets, which use an AI agent to automatically find and benchmark optimized inference configurations for your models across available hardware—you define the model and environment, and the system handles framework selection, context length tuning, and performance validation. Beyond inference, the release adds nested execution support (dstack can now manage other dstack runs from within a container), OpenTelemetry observriptions for server monitoring, and Vast.ai spot instances for more cost-effective GPU access. These additions target the workflow pain points of inference optimization, orchestration complexity, and cost management.

This release brings meaningful improvements to power management and observability across Blackhole and Wormhole architectures. On Blackhole, a new kernel throttler can now engage when hitting AICLK floor limits—disabled by default but tunable via ARC messages and firmware tables—while GDDR temperature monitoring gets more frequent updates and telemetry gains a dedicated memory region for Metal runtime data. The Ethernet stack received a solid update with alternative SerDes link speeds and a more robust recovery mechanism that escalates gracefully through retrain, reinit, and port-down states, plus API instrumentation to track function call patterns. If you're running Blackhole systems, check the migration guide to understand how the new throttler defaults interact with your workloads.

This release addresses a connectivity issue when connecting to remote Quasar servers, fixing a regression that could block remote debugging and profiling workflows. If you've been experiencing connection failures to Quasar devices over the network, this update restores that functionality.

The visualizer gains three new performance analysis tools: a histogram view for understanding operation runtime distributions, heuristic flags that surface operations worth investigating, and the ability to filter the performance table directly from charts. Behind the scenes, the MLIR viewer got snappier through memoization fixes, remote file transfers are now more robust (cleaning up stale uploads on reconnect), and remote connections can pull defaults from environment variables, making repeated debugging sessions faster to set up.

A resilience fix for Blackhole: when a broadcast write is dropped, the workload now re-broadcasts on a verify miss (#35) rather than silently carrying on with cores that never got the message — which matters for a tool whose whole job is loading every core, every time.

Blackhole telemetry lands in the display — current draw, AICLK, and power limits are now visible while the stress workload runs (#30), so you can watch a chip approach its envelope in real time. A crash is also fixed by correcting the tensix harvesting to NOC0 column mapping (#34), which had tripped up parts with harvested columns.

tt-smi now surfaces process-level visibility directly in the UI with a dedicated process listing tab and includes running processes in snapshot exports, making it easier to correlate device activity with specific workloads. The tool also gains an --offline flag for network-restricted environments and refines its internals with dedicated worker threads for process polling to avoid blocking telemetry collection. These changes move tt-smi toward more granular device monitoring while maintaining responsiveness in production environments.

Developer meetup on RISC-V: 2025–26 industry trends, an intro to the Vector Extension (RVV), and Tenstorrent's Ascalon CPU chiplet development and performance. Hybrid — Aug 27, 2026, Tokyo & online. — Tenstorrent社員が解説する開発者向け技術交流会。今回のテーマはRISC-V。業界の最新動向、ベクトル拡張（RVV）入門、RISC-VベースCPUチップレット「Ascalon」の開発と性能評価を紹介。2026年8月27日、東京オフィス＆オンラインのハイブリッド開催、参加費無料。

SFPU kernel authors get a fistful of new intrinsics: a polynomial() evaluator that takes coefficients directly, ldexp() scaling with a one-cycle-faster Ldexp::Fast mode for code that can forgo overflow handling, and a round() whose result converts to vFloat or vInt — or both at once via structured bindings. Integer work benefits too, with min, max, and clamp for vUInt plus new LO16/HI16 data-layout members for half-word access.

This release brings several targeted improvements to simulation accuracy and hardware feature support across Tenstorrent architectures. The updates span data format handling—including new BFP4 support in MOVD2A and bit-exact FP16 packer behavior—alongside fixes for mesh routing coordinates and floating-point mantissa masking in critical operations. You'll also find expanded packer format options for BH, QSR register debugging support, and fixes for edge cases like negative addressing in SFPSTORE, which collectively improve simulation fidelity for both existing and emerging workloads.

Exalens can now initialize even when devices start in an unhealthy state, removing a blocking constraint for diagnostic workflows. This is particularly useful in recovery scenarios where you need to inspect or repair problematic hardware before full operation is possible.

Booth (formerly BarraCUDA) has officially shed its pun for a clearer identity, with the compiler now sporting CPU backends for x86-64 and RV64 that handle scalar arithmetic—meaning you can compile and run CUDA, HIP, or Triton kernels on machines without a GPU. The Tensix backend now generates native machine code for the RISC-V cores instead of relying on C++ fallbacks, and device functions with control flow finally work correctly across GPU and vector targets. Diagnostics now match Clang's familiar style with colored carets, and several community contributors landed meaningful improvements ranging from HIP builtins to cross-platform build fixes.

This release brings optimizations to inverse hyperbolic functions (atanh, asinh, acosh) across fp32 and bf16 by leveraging log1p for better numerical stability and performance, while also fixing divide-by-zero edge cases in scalar math operations. The models ecosystem expanded with bring-ups of Llama-3.2-3B, Llama-3.3-70B, Qwen3-32B, and Phi-4 to TTTv2, plus a stability fix for Gemma-4, giving you more inference options on Tenstorrent hardware.

The driver now allows exclusive device access via O_EXCL flags, letting tools like tt-flash guarantee they hold the device for the entire flash-and-reset cycle without losing ownership across intermediate resets. A new experimental ioctl exports TLB windows as dma-bufs for peer-to-peer PCIe DMA from devices like RDMA NICs directly into chip memory—though the release notes carefully document the hazards when a NOC hangs mid-DMA, which can escalate to host machine-check exceptions. The update also fixes resource leaks where wedged processes could exhaust TLB and iATU pools across resets, and corrects a 58-bit address masking bug on Blackhole that was silently misdirecting pinned-buffer DMA to the wrong outbound window.

The installer now defaults to release versions instead of development builds, making fresh installations more stable and predictable for users who don't explicitly specify a version. This shift aligns the default behavior with what most users expect—getting a tested, production-ready setup rather than bleeding-edge code.

Monitor image and video generation models via tt-media-server in the new inference server visualizer. Improved rendering for each viz's legend (accessible with the l hotkey from any tt-toplike surface).

This release brings the new LLM-from-scratch lesson track as well as improvements to the existing model training material. Learn how to build and train models right on your TT-QuietBox 2.

This release refines test robustness and compiler behavior across several areas. Changes include isolating the math_reconfig test from internal pipeline state, elevating CI logging to DEBUG for better observability, and fixing a variable shadowing bug in the global/constant lookup logic. The update also makes NOC1 the default network interface and bumps the UMD dependency to 0.9.9, keeping the compiler stack aligned with upstream improvements.

tt-atom v0.2.0 adds Orb-v3, a non-equivariant attention-MPNN molecular model that runs entirely on stock ttnn ops without custom kernels, plus OrbMol, a charge/spin-conditioned variant trained on OMol25 checkpoints. Trace capture for Orb-v3 delivers 3.9–4.8× speedup over eager evaluation while remaining bit-exact to the CPU oracle, though an attempted bf8 weight quantization showed no performance gain since the forward pass is dispatch-bound rather than bandwidth-bound. The UMA code path from v0.1.0 remains byte-identical and verified on this combined build environment.

Protenix-v2 is now delivering accurate predictions after two silent bugs were fixed: the template embedder was being skipped entirely, and recycling was running 3 steps instead of the specified 10, causing a 3.87 Å CA-RMSD gap that's now closed to 1.42 Å. The release also tightens ESMC-6B multicard scaling through shared checkpoint caching and thread-pool management, and introduces an optional persistent-worker controller mode for embed that eliminates per-call weight-reload overhead—useful for batch workloads but not needed for one-off predictions. All models pass their accuracy gates, with Boltz-2 and ESMFold variants stable and performance essentially flat.

ttsim v1.9.5 brings expanded format support across the FPU pipeline, adding FP8 and BFP2 handling in more operations while introducing FP8 E4M3 support in the BH packer, alongside MOVD2B FP16 capability. The release also addresses several stability improvements: fixes to the dst_row_valid reset behavior, enhanced Tensix register code generation, and broader coverage for the ZEROACC workaround in sub-tile scenarios. Together with a new suite of Ethernet base firmware features for Wormhole and clearer error reporting, these changes make the simulator more robust for validating production workloads across diverse numeric types.

This release brings concurrent job execution to the local art generator, letting you spin up multiple generation tasks without waiting for each to finish, plus adds support for P300X2 image models and improved server discovery for the tt-inference-server integration. The upgrade streamlines workflows for developers working with generative models on Tenstorrent hardware, making it easier to iterate and experiment with batch processing.

This release expands the inference server's model portfolio across both P150 and Blackhole devices, bringing support for several competitive generative and vision models. Notable additions include Qwen3.6-27B and Gemma-4-31B for dense inference on dual-card Blackhole setups, along with lighter models like Falcon3-7B on P150, while also uplifting existing deployments like Llama-3.1-8B and FLUX.1-schnell to the latest TT-Metal stack. Most new models are marked experimental, but YOLOX-nano has reached completion status, showing the team's progress in stabilizing inference implementations across different architectures and device configurations.

tt-bio v0.2.4 brings device-resident trunk optimization to BoltzGen, keeping token-distance injection on the accelerator to reduce host–device roundtrips from four to two per iteration—cutting total wall-clock time by ~31% without changing any model behavior. The new TokenDistanceRecycle module is opt-in via use_resident_trunk=True (the default), so existing workflows stay stable; all three structure models (Boltz-2, ESMFold2, Protenix-v2) and the BoltzGen designability pipeline pass release gates with no regression versus v0.2.3. This is a solid efficiency win for researchers running iterative folding and design workloads on Tenstorrent hardware.

The v0.2.3 release adds multi-card fanout parity to predict and a designability verification script for structure generation, while polishing the embed command's input handling to accept YAML mappings and bare sequences alongside FASTA files. The core structure-prediction models are byte-identical to the previous version, so the release gate—which confirms all four models (Boltz-2, ESMFold2, Protenix-v2) pass their accuracy thresholds—is a validation run rather than a re-verification. Performance holds steady at 43.4s for Boltz-2 fast inference on mid-size proteins, and the full test suite passes with no memory issues or regressions across the supported protein size range.

This release ships improved device initialization and simulator support. Key fixes include resolving false-positive NOC hang detection that was causing timeouts on healthy devices with tight MMIO budgets, and handling harvested cores correctly so tests don't abort when reading from disabled memory banks on SKUs like Blackhole p100. On the simulator side, the changes unify TLB window setup between RTL and TTSim backends and allow remote devices to accept SocDescriptors directly, laying groundwork for better simulation parity. The release also introduces sccache integration for faster CI builds and adds microbenchmark tests for TTSim clusters.

dstack now supports Slurm as a backend, letting you orchestrate container workloads across existing Slurm clusters by connecting to login nodes over SSH and submitting jobs—each cluster becomes its own dstack region. The release also bumps the CUDA runtime in dstack's base Docker images from 12.8 to 13.0, though this drops support for Volta (V100) and older architectures, so you'll need to bring your own CUDA runtime or custom image if you depend on older GPUs. Several gateway configuration methods have moved to plan-based APIs, and the Cudo backend has been removed.

tt-bio 0.2.2 makes multiple-sequence alignment the default for Boltz-2 and Protenix-v2, correcting a silent accuracy regression that had shipped in earlier versions—the models now use a local ColabFold database when available, with a privacy-aware fallback to the online server if needed, and users can opt out with --single_sequence for single-protein screening. The release also ships multi-card embedding support for ESMC, letting you shard sequence sets across multiple TT cards with bit-exact reproducibility against single-card inference, making large-batch embedding workflows substantially faster without numerical drift.

tt-bio 0.2.1 ships ESMC protein embeddings via both CLI and Python API, with automatic batching that achieves ~18.5x throughput improvement over single-sequence processing while maintaining bit-identical masked outputs across the 300M/600M/6B model variants. The release also corrects stale packaging metadata—the PyPI description now accurately reflects all five supported capabilities (Boltz-2, ESMFold2, Protenix-v2, BoltzGen, ESMC) and the README now prioritizes pip installation as the primary path. Embeddings achieve 0.9995–0.9999 Pearson correlation parity with reference ESMC implementations, so they're ready for downstream tasks that don't require structure prediction.

The simulator now builds on Ubuntu 24.04 and gains Ethernet base firmware simulation for Wormhole, letting you test firmware routing behavior in simulation via an environment variable while also fixing several hardware model issues including DBG_ARRAY_RD correctness, iATU translation bugs, and denormal flushing edge cases across FPU operations. Beyond the core fixes, the release expands instruction support on QSR with new SFPSTORE and SFPENCC implementations and enables several previously untested code paths in GMPOOL, making the simulator more representative of actual hardware behavior.

tt-bio v0.2.0 is now validated on Blackhole hardware with real-weight parity across Protenix-v2, Boltz-2, and ESMFold2 components—all 14 Protenix submodules match reference behavior with an 8.7 Å Kabsch RMSD well within expected variance, and the suite runs without OOM across supported model sizes. The release adds opt-in tracing for the Protenix denoise diffusion stream (achieving ~22% faster warm sampling at L256), migrates control toggles from environment variables to normal function arguments for cleaner APIs, and hardens input validation to catch edge cases like duplicate chain names and out-of-range device IDs. These changes tighten the integration between tt-bio and Tenstorrent hardware while making the library more robust for production use.

The visualizer now lets you watch inference server models load in real time and track their performance as they run, giving you immediate insight into what's happening on your hardware. Remote sessions are now supported, so you can run tt-toplike on a separate machine from your Quietbox 2 without needing SSH—just point your client at a server instance and monitor from anywhere on your network. The release also includes performance improvements and better detection of when your inference server is ready to go.

tt-atom brings Tenstorrent inference to Meta's UMA equivariant interatomic potentials, with energy, forces, and stress calculations exposed through an ASE calculator interface that makes swapping off fairchem trivial. The implementation fuses the per-edge Wigner rotation into a custom tt-metal kernel—delivering 4.3× speedup on that operation alone and 1.4–1.68× end-to-end gains for MD and relaxation loops up to N=2662 atoms, while matching fairchem's accuracy to within 5.4e-4 relative error on energy. The v0.1.0 release targets uma-s specifically with no silent fallbacks, includes a device-resident trace loop for multi-card throughput, and ships verification and checkpoint-conversion tools so you can validate round-trip correctness before production use.

The visualizer adds several conveniences for working with MLIR graphs: regex filtering on op names alongside the existing substring mode, overlay toggles for source locations and tensor shapes, and expand/collapse-all buttons for navigating subgraphs. Upload workflows are more resilient now—failed file transfers can be retried without re-selecting the file, and reports now capture their source repository and commit hash for better traceability. The docs site also gained a proper navigation bar and search, while the UI stayed focused by moving the JSON upload option behind dev mode.

Crossing between integer and float vectors just got easier — two-step conversions between vInt and vFloat are now built in, closing a gap kernel writers previously papered over by hand. Under the hood, instruction scheduling improved for the sfpiadd_i, sfpshft_i, and sfpswap builtins, which should shave cycles off tight SFPU loops without any source changes.

The P100 DRAM training check in tt-smi has been refreshed to reflect the latest hardware validation data, ensuring more accurate diagnostics when assessing device health. This update helps operators quickly identify and understand DRAM status on P100 devices, making it easier to catch potential issues early in your deployment workflow.

FP8 support in the WH/BH unpacker and corrected handling of denormals and truncation in several ops brings ttsim closer to real hardware behavior, while a handful of infrastructure fixes—like proper reset PC defaults and ARC telemetry address corrections—make simulation more reliable for low-level debugging. These improvements should reduce friction when verifying kernels that rely on mixed-precision arithmetic or when instrumenting TRISC/NCRISC execution on newer architectures.

The installer now derives firmware URLs directly from the FW_REPO configuration, eliminating hardcoded paths and making it easier to track firmware releases across different environments. A new composite CI action for verified-baseline installs has been added to streamline testing, alongside documentation clarifying how to specify Python versions during installation—useful if you're working with multiple Python environments on the same system.

This release tackles several foundational issues: a corrected ROUTER_ONLY base address for Blackhole, fixed overlapping memory map intervals in Quasar that were likely causing analysis conflicts, and a shift away from custom 4B timeout handling in favor of UMD's timeout detection implementation for better consistency across the stack. The team also re-enabled debug bus and NOC1 tests that had been disabled, plus updated mypy to catch more type issues—changes that collectively improve both reliability and maintainability of the analysis toolchain.

Time to modernize old kernels: the vConst0, vConst1, vConstNeg, and vConst0p8373 constants are deprecated in favor of plain float literals (explicitly converted to vFloat where needed). The documentation was refreshed alongside, so the current idiom is written down rather than folklore.

The v0.9.8 release ships C++ libraries, system packages for Ubuntu and Fedora, and Python wheels across multiple architectures and Python versions (3.9–3.13). The release consolidates simulation device infrastructure, renaming set_power_state to set_clock_state and introducing a SimulationTTDevice base class to unify RTL and tt-sim backends; it also adds per-operation MMIO timeouts with hang detection to catch slow device degradation during TLB-mapped transfers, configurable at runtime via MmioTimeoutConfig. Several refactors tighten the API surface—deprecating xy_pair coordinate methods in favor of CoreCoord, widening RemoteChip to accept any Chip base, and removing duplicate multicast implementations—while CI improvements add performance regression detection and switch to free runners for builds.

The MLIR viewer gains a live op-name filter with substring search, match badges, and keyboard navigation, while multi-file uploads now show per-file processing status with a results picker. The performance table adds per-op kernel timing with colors aligned to tt-perf-report, and operation rows display inline kernel-duration bars for quick visibility into hot ops. Cluster visualization now handles multi-host topologies by reading mesh coordinates and routing inter-host ETH links efficiently, while the graph processor fix ensures remote-rank ops land correctly in multi-host runs—these changes bring the visualizer closer to production-grade multi-device support.

tt-toplike now builds properly on macOS with improved --host rendering support, making it easier to visualize your workloads on Mac hardware, even if they aren't Tenstorrent-oriented. Watch out, observing can change the visualizations in fascinating ways.

The debut release of Tenstorrent's Kubernetes operator. tt-operator automates installation and lifecycle of the software components needed to run TT workloads on a cluster — packaged as an umbrella Helm chart spanning driver (tt-kmd) management, Node Feature Discovery, firmware flashing, telemetry, Fabric Manager, and device allocation with multi-node scheduling. This release migrates tt-telemetry into the helm/ path and bumps the umbrella subcharts to their latest releases.

New documentation covers running Tenstorrent accelerators on Kubernetes via tt-operator, the umbrella Helm chart that installs and coordinates the full stack: Node Feature Discovery for hardware-aware node labeling, kernel-mode driver (tt-kmd) management, firmware flashing, Prometheus telemetry, Fabric Manager topology resolution, Dynamic Resource Allocation for schedulable devices, and multi-node scheduling with JobSet and PMIx. Supports Blackhole and Wormhole PCIe accelerators.

TT Studio v2.8.0 brings coding agents directly to the platform—Claude Code and OpenCode are now deployable models that can write, debug, and review code alongside your other workloads, all running on LiteLLM for consistent performance. The release also expands video generation with WAN T2V and Flux support, adds whole-board deployment for Wormhole mesh systems, and lets you run multiple instances of the same model simultaneously, while cleaning up several rough edges around cold-start latency, device ID handling, and resource cleanup.

Luwen's bh-mod support is now production-ready, bringing a new interface layer for managing Blackhole modular systems. The release introduces kernel throttler parameter exposure and improved versioning tracking (reporting both bh-mod and luwen versions), making it easier to correlate issues across the stack and tune performance characteristics. A dependency bump to bytes 1.11.1 and a new CI pipeline round out the stabilization work.

The tt-vscode-toolkit now includes an expanded CS Fundamentals Module 8 focused on matrix multiplication, bundling interactive lab content, execution guidance optimized for the simulator, and improved navigation wiring throughout the course materials. This release adds code-first host examples with step-by-step breakdowns and tooling to detect when upstream matmul lessons drift out of sync, making it easier for learners to work through practical matmul implementations without hunting for scattered resources.

The official onboarding guide for the TT-QuietBox 2 — a liquid-cooled desktop workstation with four Blackhole accelerators — is now live, organized into four hands-on learning paths: Explore (first look at the hardware and software, 6 chapters), Run & build (deploy pre-built models, 6 chapters), Tinker (dig into model architectures and the tt-metal / tt-inference-server stack, 6 chapters), and Customize (make the workstation your own, 5 chapters). It ships with Qwen3-32B pre-loaded and walks through running text models (Llama 3.x, Qwen3 4B–32B, Gemma 3, GPT-OSS), image generation (FLUX.1, SDXL), video (Mochi 1, Wan2.2), and speech (Whisper-large-v3, SpeechT5) — all served through tt-inference-server.

The installer now defaults to Docker as its container runtime instead of the previous option, streamlining setup for most users while maintaining flexibility for those with different requirements. This change simplifies the initial configuration experience and aligns with Docker's widespread adoption in development environments.

This patch fixes an idempotency issue in the autoInit method for single-chip setups, ensuring that repeated initialization calls don't cause unintended side effects. If you're using TensixViz for automated visualization workflows on single-chip systems, this prevents state inconsistencies when initialization gets triggered multiple times—a common scenario in development and testing environments.

This release brings steady progress on model coverage and infrastructure improvements, with updated dependencies across tt-xla, tt-mlir, and tt-metal. The team has added several new model demos—including Llama, Tiny Llama, and Qwen3—alongside expanded ecosystem skills for AI workflows. Behind the scenes, there's been a focus on testing and CI/CD refinement: installation validation now uses AI-driven testing, the benchmark infrastructure has been removed to streamline the codebase, and AI bringup scripts have migrated to a dedicated repository to keep concerns separated. The performance tables show broad support for LLM inference across architectures like Falcon, Llama, Qwen, and Mistral at various model sizes, plus solid coverage for vision and multi-modal tasks on both n150 and p150 hardware.

This release brings substantial improvements to the vLLM plugin and infrastructure, alongside broader model coverage and performance tuning across the ecosystem. Key enhancements include KV cache dtype defaults (now bfp_bf8), decode graph optimization (reduced from 5 to 2), and skip mechanisms for wasteful profile runs that improve both compile time and throughput. Multihost support landed with deferred tensor transfers, vLLM warmup phase optimization, and better diagnostics through logging. The team also added support for new models like Mixtral and Pixtral in the vLLM plugin, expanded multimodal capabilities (Rotary Embedding with multimodal sections), and brought component tests online for major diffusion models including Mochi-1, HiDream, Playground v2.5, and various video generation pipelines—all while maintaining an aggressive push toward stability through better error handling, RMSNorm fusion patterns, and refined test categorization across n150, n300, and p150 hardware.

This release brings synchronized updates across tt-mlir and tt_forge_models dependencies, along with test infrastructure improvements and a fix for ResNet50 benchmark regressions that addresses device teardown segfaults and layout configuration issues. The team also refactored ONNX test suites and corrected a verify config bug that was writing to the wrong global variable. You can grab the update via PyPI or Docker, and the changes span from late May through late June.

See how Blackhole's switch-free architecture extends the low-cost serving curve by unifying compute, memory, and networking into a single scalable system. Presented by Jasmina Vasilović, Senior Fellow, ML, Compilers and Models at Tenstorrent. Learn more about Tenstorrent Galaxy™ Blackhole: https://tenstorrent.com/hardware/galaxy

The simulator now covers more hardware configurations and instruction variants across Tenstorrent architectures, including pack format modes, ReLU intermediate formats, and expanded ADC/RWC support on Wormhole and Blackhole devices. Recent additions handle debug bus access to multiple subsystems, Tensix replay buffer wrapping, and 64-bit atomics in QSR DRAM cores—changes that help developers more accurately validate their workloads against actual silicon behavior. The release also patches around a known Wormhole/Blackhole erratum and improves error reporting for edge cases, making the simulator a more reliable reference for hardware-software codesign.

The inference server now runs a broader set of models across Blackhole QuietBox 2 hardware, with support added or uplifted for everything from the large language models (gpt-oss-120b, Llama-3.1-8B-Instruct, Qwen3-32B) to multimodal and generative workloads (Z-Image-Turbo, FLUX.1-dev, Wan2.2-T2V-A14B). Wormhole Galaxy systems also gain uplifted implementations of whisper speech recognition models. The update brings the stack to a consistent TT-Metal commit and includes revised software version recommendations for Galaxy deployments, ensuring developers have a stable baseline for production inference across the hardware lineup.

The installer now bundles the setup script directly into the main installation script at build time, eliminating an external dependency and simplifying the installation flow. This consolidation makes the installer more self-contained and reduces moving parts during the setup process.

The installer now includes a formalized schema (v1) that standardizes how installation configurations are defined and validated, along with comprehensive tests to ensure reliability. This structured approach should make it easier to maintain consistent installations across different environments and lay groundwork for future tooling that can work with installation metadata programmatically.

The player schema handling has been fixed to ensure that autoInit operations are truly idempotent, preventing unexpected state issues when initialization runs multiple times. This is particularly useful if you're working with visualization tooling that might trigger initialization in unpredictable patterns, as you can now rely on consistent behavior regardless of how many times the setup runs.

tt-umd v0.9.7 adds several new error types for firmware diagnostics (heartbeat, routing config, and mismatch detection) and exposes DeviceTimeoutError to Python so applications can catch and handle per-operation MMIO timeouts gracefully. The release refactors device construction and protocol layers to push more logic into TTDevice for consistency across backends, fixes coordinate translation for harvested Ethernet cores on Blackhole, and introduces SimulationSocket for per-chip simulation sockets and OpTimeoutGuard as a reusable timing primitive. System packages are available for Ubuntu 22.04/24.04 and Fedora 39, alongside Python wheels for 3.9–3.13 across x86_64 and aarch64 architectures.

This release focuses on infrastructure improvements, architectural support expansion, and numerous bug fixes across the tt-metal stack. Key highlights include bringing up Quasar architecture support with its SFPU compute APIs, migrating dataflow kernels to the Device 2.0 NoC API, and stabilizing DeepSeek prefill operations with optimizations for multi-chip deployments. The team also made significant progress on descriptor framework migrations for better program caching and resolved critical issues in sampling, MoE operations, and layer normalization that were causing device hangs and accuracy regressions. This is primarily a foundation-focused release with substantial internal refactoring to enable future feature work.

dstack now reuses SSH connections to instances through a server-side pool, cutting the per-operation overhead that previously slowed down runs, dev environments, and services — the improvement is especially noticeable on servers managing many instances. The release also speeds up the runs listing endpoint, making dstack ps and the UI snappier for projects with large run histories, and fixes AWS Capacity Reservation support by properly applying the reserved tenancy when launching instances.

The Buffer Summary timeline now ranks ops by kernel duration, op-to-op gaps, DRAM utilization, FLOPS, or L1 fullness with color-coded badges, making it easier to spot bottlenecks at a glance. The visualizer also cleans up the MLIR upload flow with clearer progress messaging, fixes a multihost bug that was silencing non-host ops from the graph capture, and improves UI density by letting you toggle globally allocated circular buffers out of view when they clutter the per-core grid—plus several layout refinements to the cluster view and legend alignment that reduce visual overlap.

This release brings the VS Code Toolkit's lesson and cookbook content fully in line with Ubuntu 24.04 and QB2 environments by fixing Python interpreter references, venv activation paths across three deployment contexts (tt-developer-image, QB2 pre-installed, and cloud setups), and device management patterns that were triggering dispatch errors on multi-chip teardown. The updates touch seven major lessons—from game-of-life basics to vllm production deployment—ensuring developers can follow along without path or interpreter mismatches regardless of their setup. A particularly important fix replaces the broken per-chip device opening loop in cookbook-particle-life with coordinated CreateDevices/CloseDevices calls, eliminating a common source of teardown crashes on multi-device systems.

The v0.6.3 release brings performance improvements to the Defrag operation while sharpening reactivity features, making the tool more responsive when you're monitoring and managing your Tenstorrent systems. These optimizations build on the foundation laid in previous releases, ensuring smoother interactions as you work with larger or more complex device configurations.

The ttsim-QEMU bridge tooling gets steadier here. The install check no longer shells out to grep, using a pure-Node substring match instead, so it works regardless of whether grep is on PATH. The free-disk-space check now measures the actual ttsim-qemu target directory rather than your home folder — which matters when ~/sim lives on its own partition — and the threshold moves up to 12 GB to fit the 10 GB image. The qemu-bridge lesson was also reworded to make clear that the frictionless “pip install ttnn and go” flow is the intended end state, not where things stand today.

The local generator now ships with tt-animatediff v0.9.0 and introduces an assistive installer to streamline setup, alongside UI enhancements that should make the tool more intuitive to work with. A housekeeping change also adds the root .env file to gitignore, preventing accidental commits of local configuration.

This release adds support for Orion SLT silicon, expanding tt-flash's compatibility across Tenstorrent's hardware lineup. If you're working with Orion SLT devices, you can now use tt-flash for firmware management and deployment workflows without workarounds.

AnimateDiff now includes a Gradio-based web interface alongside full documentation and test coverage, making it more accessible for developers exploring text-to-video generation on Tenstorrent hardware. The addition of Lightning support streamlines integration with existing training workflows, reducing friction for those looking to fine-tune or experiment with animation models at scale.

The simulator now gives RISC-V tooling better access to device configuration through expanded PCI register support and --tt-device flags, while fixing SMP synchronization issues that were blocking real workloads. On the compute side, the QSR DM core can now use vectored exceptions for more efficient interrupt handling, and floating-point edge cases—like denormal flushing in SFPSTORE—now match hardware behavior more closely.

Tenstorrent Galaxy™ is now part of the Equinix Distributed AI Hub™. See how Equinix, BetterBrain, OrionVM, and Tenstorrent are building infrastructure for agentic AI: https://tenstorrent.com/hardware/galaxy

This release focuses on performance and debugger reliability across the board. The team refactored CMake configuration for tt-metal compatibility, moved callstack introspection to C++ for efficiency, and tackled flaky test failures on Wormhole EriSC and low-clock scenarios. Key improvements include faster MemoryMap operations, better frame reconstruction with tail call and inline frame expansion support, and more robust GDB server behavior in the test harness—changes that should make debugging Tenstorrent workloads smoother and more predictable.

We've released ttsim-riscv64, a lightweight full-system simulator that can boot Linux, giving developers a faster way to validate RISC-V workloads without hardware. Beyond that, we've expanded register support across ARC, PCIe, and tile domains, and added two important Tensix features: pack format conversion mode 0x111 and SFPSTORE mode LO16, which unlock new data manipulation patterns for compute kernels.

We built an agentic AI pipeline that continuously ports, compiles, and validates models from Hugging Face on Tenstorrent hardware. After thousands of models tested, the pass rate remains above 90%. Learn how with TT-Forge: https://tenstorrent.com/software/tt-forge

dstack now runs workloads on Ubuntu 24.04 by default, bringing your containers up to date with the latest LTS release while allowing explicit pinning to 22.04 if needed. The release adds three practical features: instance targeting to pin runs to specific fleet members (useful for instance-volume dependencies), gateway replicas for improved availability, and native support for AWS p6-b200 and p6-b300 instances including the new NVIDIA Blackwell B300 with 6.4 Tbps networking pre-configured for maximum throughput.

Prodia generated an 81-frame 720p video in just 2.4 seconds on Tenstorrent Galaxy™ Blackhole. See how we generate video faster than real time: https://tenstorrent.com/solutions/real-time-video

The visualizer gains early beta access to tt-mlir's model explorer for end-to-end MLIR workflows, while circular buffer visualization gets a substantial upgrade with per-core pressure modals and corrected envelope calculations that prevent double-counting of globally allocated buffers. Several stability fixes address stale focus states in MLIR views and mutation bugs in tensor sharding visualizations, plus the graph_report tool can now merge captures from multi-host runs while tracking rank information. The project has also switched to UV for Python dependency management, making local setup smoother for contributors.

This release improves error handling by ensuring non-zero exit codes propagate to the shell on failures, making it easier to catch issues in scripts and CI/CD pipelines. The README install instructions now point to uv for dependency management, aligning with modern Python packaging practices.

The simulator now covers more of the hardware stack, with expanded ARC, PCIe, Ethernet, and tile register support bringing its model closer to real silicon behavior. A few Tensix fixes and clearer error messages should also smooth out debugging workflows for developers working through simulation.

TT Studio v2.7.0 brings several quality-of-life improvements centered on deployment visibility and state management. The Voice Agent now supports wake-word and voice-activity detection for hands-free operation, while deployment progress tracking extends across Docker image pulls, container startup, and media model loading to give you continuous feedback. Behind the scenes, the tool cleans up its state handling with a unified source of truth for read operations, splits cleanup into granular (--cleanup) and full-reset (--cleanup-all) modes, and fixes lingering issues like stale startup detection, persistent terms acceptance, and accurate model performance reporting.

This update broadens the simulator's coverage of the chip's management and host-interface paths, filling in more ARC, PCIe, and tile register behavior so a wider range of low-level workloads exercise the same registers they would on real silicon. A round of minor Tensix fixes and small feature additions tightens compute-engine accuracy, and clearer error reporting makes it easier to tell why a run went wrong rather than just that it did.

The v1.8.3 release tightens up Ethernet and memory management in the simulator. Fixes to eth base firmware interactions in both idle and active erisc code eliminate quirks that could have masked real hardware behavior, while proper ARC_MSG_QCB_PTR register support brings the simulation closer to actual device firmware. Removing the MMIO DMA hack in favor of outbound iATU configuration is a meaningful architectural improvement that lets you test more realistic DMA scenarios across multiple chips. The addition of pack format conversion mode 0x15 for Tensix expands format coverage for those working on tensor operations.

We've uplifted Llama-3.3-70B-Instruct support on Blackhole QuietBox 2, with the implementation now running on P300X2 devices via tt_transformers. If you're working with Wormhole Galaxy, make sure you're on tt-smi 4.0.0, firmware 19.2.0, and tt-kmd 2.5.0 to stay compatible with this release.

This internal testing build preserves hotplug support on Galaxy systems, a shift from earlier behavior where hotplug was disabled during driver loads. If you're upgrading from KMD 2.8.0 or 2.9.0, a clean installation is required—uninstall the old driver completely before installing this version and rebooting—to ensure the older hotplug-disabling code doesn't persist on your system.

dstack now lets you pin runs to specific fleet instances instead of always provisioning new resources when your preferred instance isn't available. You can target instances by name, hostname, IP address, or fleet reference—including instances from shared projects—giving you finer control over where workloads land and helping optimize for cost or data locality. This is particularly useful when you want to ensure a run uses a specific pre-configured machine or avoid triggering unnecessary provisioning.

tt-smi v5.3.0 brings better hardware monitoring and PCIe management capabilities. Fan speed reporting now shows RPM values instead of percentages across n300d and n150d devices, with datasheet-derived limits for more accurate thermal tracking. The release also adds RESET_PCIE_LINK support for Galaxy systems running compatible KMD versions and consolidates driver version checking and reset functionality directly into tt-smi, streamlining the toolchain.

ttsim now simulates the larger Wormhole and Blackhole multi-chip configurations, with the new wh_x32 and bh_x32 setups covering 32-chip Galaxy deployments. The release also expands host communication capabilities by adding outbound iATU, DMA, and host-to-device multicast support, while patching several fabric and multichip issues that affected P300 and T3000/LoudBox systems. This rounds out the simulator's coverage of Tenstorrent's production hardware and lets developers validate larger cluster topologies earlier in their workflow.

This release brings Zephyr 4.4.0 to Blackhole and Grendel, along with several quality-of-life improvements for Blackhole operations. Error reporting is now more granular—the STATUS_ERROR_STATUS0 register now accumulates init failures as a bit field so you can see exactly which stage failed (regulator, cable, Tensix, MRISC, or GDDR training) in a single read. Runtime firmware parameters like TDP limits can now be overridden and persisted across upgrades, though you'll want to be on tt-flash v3.8.0+ to ensure those overrides stick. On the connectivity side, P300C boards get PCIe Gen4 for better QB2 stability, and Ethernet firmware improvements address interrupt handling and add ECC support to MACPCS.

This release fixes a bug where the program counter couldn't be read reliably when a core was halted, and extends UmdApi to properly handle core reset logic on the QUASAR architecture. The test suite also gains conditional skipping for scenarios that don't apply to certain configurations, reducing noise in CI runs and making it easier to focus on relevant failures.

This release brings several refinements to dev environments and service deployments. Zed is now supported as a remote IDE option, with automatic server setup and convenient zed:// links for quick connection—useful if you're already in that editor ecosystem. On the services side, you can now specify spot_policy and reservation at the replica group level, enabling mixed strategies like running baseline replicas on reserved capacity while autoscaling overflow on spots; additionally, Shepherd Model Gateway workers now communicate via gRPC with both vLLM and SGLang, which reduces duplicate tokenization work and makes request routing more efficient. Azure users gain finer subnet control, JarvisLabs adds RTX PRO 6000 GPU support, and several reliability fixes address SSH connection pooling and service scaling edge cases.

Learn how you can simulate Tenstorrent hardware! Follow along with all 31 examples in https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/ttsim-twenty-and-ten/

This release brings a Gradio UI frontend to the AnimateDiff implementation, making it easier to interact with the model through a web interface rather than command-line tools. The team also fixed layout responsiveness issues and laid groundwork for Lightning mode support, which will enable faster inference on Tenstorrent hardware in a future update. These changes lower the barrier to experimenting with AnimateDiff while keeping the door open for significant performance improvements.

This release improves the visualizer's reliability and usability across several fronts: better error messaging with scp fallback for sync issues, smarter graph rendering through edge deduplication and linked node navigation, and more flexible performance analysis with toggleable columns and graceful data reloading. You'll also find stronger support for diverse profiler formats and buffer allocation breakdowns, including pre-aggregated chunk handling and per-core CB calculations, plus cleaner stack trace navigation and more robust file handling across rank suffix variations.

The simulator now models larger Blackhole and Wormhole configurations, letting you test code targeting the 2-chip P300 and 8-chip T3000/LoudBox systems before running on hardware. A new multicast capability in host-to-device TLBs enables more realistic memory access patterns, and several Tensix core fixes improve overall simulation fidelity.

This release adds performance counters support for deeper profiling visibility, along with hardware-specific fixes for Wormhole and Blackhole devices that improve stability across different architectures. Thread-local storage now works reliably across multiple cores, and improved gcov integration makes coverage analysis more straightforward for developers validating their code paths. These changes round out the debugging and analysis toolkit for those working directly with Tenstorrent hardware.

This release brings visual refinements and usability improvements to the hardware visualization tool. You can now toggle between light and dark modes with improved contrast handling, while heatmap rendering gets a fix and responsive sizing so the visualization adapts better across different screen sizes. The update also includes security hardening to make the tool safer for production use.

v0.7.4 ships a plugin system that lets developers extend tt-local-generator with their own generation flows, alongside a new in-app log viewer and menu improvements that make the UI easier to navigate. Remix functionality is also enhanced in this release, giving users more control over iterating on generated outputs. Assets are distributed as .deb packages covering Flux, Mochi, Qwen3, SkyReels, Wan2, and AnimateDiff models.

Simulation support grows to cover multi-chip setups. The ttsim upgrade to v1.8.0 pulls down a new libttsim_wh_x2.so binary for a two-chip N300 Wormhole mesh alongside the existing single-chip Wormhole and Blackhole libraries. A new lesson walks through opening a MeshDevice(1, 2) and running a sharded element-wise add across two virtual chips, with a ready-to-run template and the matching cluster descriptor now copied into place during setup. It's a practical on-ramp for exercising mesh code without physical multi-chip hardware.

ttsim v1.8.0 extends the simulator's multi-chip capabilities with support for N300 (2-chip Wormhole) under the new wh_x2 configuration, and adds the RISC-V floating-point (F) extension to the Blackhole/QSR babyrisc model. Additional tile register functionality and another round of Tensix and QSR bug fixes continue to improve simulation fidelity.

tt-metal v0.72.0 adds Wan2.2-distill (LightX2V), Index-AniSora V3.2 video generation, and a LoRA adapter pipeline for Wan2.2 Image-to-Video, alongside SFPI 7.50.0. A new Metal 2.0 factory adapter and a fused DeepSeek MoE reduce-NC operation lay groundwork for upcoming architecture work, while targeted fixes address conv3d garbage output on batch-size changes, Gemma3-4B accuracy, DEVICE_PRINT linkage on Quasar, and a LoRA fusion state-dict corruption bug.

tt-kmd 2.9.0 brings the power-management framework from Blackhole 2.6.0 to Wormhole — placing devices in low-power state at probe and re-aggregating power flags on every open/close. The release also adds deferred idle power-down via a delayed work item, read-only DMA pinning for file-backed memory, and protection for telemetry reads against concurrent resets. Wormhole users must be on CMFW 19.10.0 or later for the new power policy to take effect.

tt-toplike v0.6.2 fixes power reporting by overlaying SMBUS TDP and TDC values onto the sysfs telemetry path, giving a more accurate picture of actual power consumption on Tenstorrent hardware. The release also adds an AI vendor-agnostic configuration setting for developer tooling flexibility.

ttsim v1.7.3 expands ARC and tile register coverage in the simulator and delivers another round of QSR and Tensix bug fixes with improved error reporting — incremental improvements that keep the simulation tracking closely with hardware behavior.

A web-build fix repairs broken media on the published site. Image, video, and link targets derived from GitHub-blob URLs were being emitted as bare /assets/img/... paths, which resolved to the domain root instead of the project's GitHub Pages sub-path and left assets like the particle-life simulation GIF broken. Those paths now run through the siteUrl() helper so they carry the correct base prefix, and a new link-validator test guards against missing local targets slipping through again.

tt-exalens v0.3.21 moves ELF and DWARF parsing from Python to C++, improving debugger performance and callstack evaluation accuracy. Reading and writing via the debug module's system bus is now enabled, remote communication tests gain data-consistency verification after halting RISC debug, and the GDB binary is removed from wheel packaging to reduce distribution size.

dstack 0.20.23 ships a tidy set of bug fixes and reliability improvements — proxy environment variables now correctly pass through to running containers, image pull progress reporting is more accurate, and latency in the run provisioning pipeline is reduced so workloads spin up faster. A fix for Verda spot offers being incorrectly marked unavailable rounds out the release.

tt-animatediff v0.1.0 is the first public release of AnimateDiff running on Tenstorrent hardware, opening up image-to-video generation to the Tenstorrent developer community. The release includes initial repository scaffolding, bug fixes, and documentation to get started.

This study addresses on-device inference bottlenecks of Transformer models on Tenstorrent's Tensix architecture and proposes an operator fusion strategy that enhances data locality. RMSNorm is fused with matrix multiplication in self-attention and in the FFN, enabling back-to-back execution of memory-bound and compute-bound operators in on-chip SRAM to significantly reduce DRAM reads/writes of intermediate results and scheduling overhead. To support multi-core parallelism, a NoC-based multicast mechanism is leveraged in which row/column master nodes efficiently distribute inputs and weights across the core mesh, alleviating DRAM bandwidth contention. Experiments on the Wormhole platform with Qwen2.5-0.5B, Qwen3-0.6B, and Qwen3-4B show up to 37.44% latency reduction for attention and…

Generates short, temporally coherent animated GIFs using the AnimateDiff model on Tenstorrent hardware. Phase 1 runs the correct SD 1.4 + MotionAdapter architecture on CPU; Phase 2 accelerates spatial denoising on Blackhole using the TTNN UNet. Produces vibrant 8-frame animations in ~15 s/frame on a P300C.

The TTNN Visualizer adds L1 fragmentation pressure to the performance table, a performance overlay on the graph view showing per-operation device time, and inspectable MLIR nodes with attribute/input/output side panels. Navigation through performance charts also gets a new in-page dropdown for easier exploration.

tt-umd v0.9.6 broadens platform support with new manylinux ARM64 (aarch64) Python wheels alongside existing x86_64 builds, making it easier to develop on a wider range of machines. Buffer handling in noc_read and dma_read gains enhanced guards, the Chip and TTDevice constructors are refactored for cleaner separation, and ethernet broadcast no longer depends on the full Cluster stack. Available as DEB/RPM packages and Python wheels for Python 3.9–3.13.

tt-toplike v0.6.1 polishes the terminal monitoring interface with layout jitter fixes that keep the display steady during live updates, enhanced compact mode for smaller terminal windows, and improved firmware version display — small but meaningful ergonomic wins for anyone watching their Tenstorrent cards in a busy terminal.

ttsim v1.7.1 adds the Zbc (carryless multiplication) RISC-V extension to the QSR RV64 model, bringing the simulator closer to full ISA coverage. The release also delivers expanded ARC and PCIe tile/TLB functionality, a round of Tensix bug fixes, and improved error message reporting throughout the simulation stack.

Blackhole gains a new TT_SMC_MSG_TOGGLE_ETH_RESET API for resetting ethernet tiles, while Wormhole gets power improvements including linking AICLK_BUSY to the max AI clock bit. Board-specific documentation is now auto-generated from protobuf definitions, and DRAM low-power mode is disabled on instances prone to slow retraining.

tt-flash v3.8.0 adds an important safety improvement for Blackhole systems: the CCFG override (ccfgovr) table is now preserved across firmware flashes, preventing accidental overwrite of critical configuration settings. A buffer boundary fix in boot_fs prevents read_tag from walking past the end of the buffer, closing a potential crash path during flash operations.

tt-exalens v0.3.20 brings Rocket Core support for debugging the new RISC-V implementation in Blackhole, adds an aarch64 build for SFPI (bumped to version 7.49.0), and makes ELF parsing thread-safe. Remote chip performance on T3K configurations sees concrete improvements, and a JAL offset calculation fix in the RISC-V debug path closes a correctness bug.

BarraCUDA's first tagged release brings a CPU backend that lets you compile and run full Triton kernels—matmul included—on x86-64 without a GPU, alongside new RISC-V support and a cross-backend differential testing suite that treats CPU as ground truth. The project now targets AMD, NVIDIA, Tenstorrent, and experimental Intel/Apple backends, with validated kernels running on real MI300X and RTX hardware; the Triton frontend gained scalar math intrinsics, constexpr parameter folding, and better CUDA compatibility, while the runtime layer added HSA support with no compile-time ROCm dependency and creative debugging tools like ABEND dumps and bilingual error messages.

The inference server now ships its first Helm chart, making it straightforward to deploy any supported model spec on Kubernetes. This release also brings updated support for SpeechT5 TTS, Whisper large-v3, and Distil-large-v3 across both P150 and the new Blackhole QuietBox 2 hardware.

The first tagged release of BarraCUDA introduces CPU and RISC-V backends for Triton kernels — you can now compile a tl.dot matmul and run it natively on x86-64 or under QEMU with no GPU required. Cross-backend differential testing is included from the start, using the CPU backend as an oracle to verify correctness across compilation targets.

The AnimateDiff example gets a round of correctness and portability fixes. Cleanup now calls close_mesh_device to match the MeshDevice it actually creates, and the ttnn import was made lazy so the module loads on CPU-only machines like CI runners. Several docstrings and comments were corrected to reflect that frames are denoised sequentially and that the Phase 3 path still bounces tensors to CPU, and stale setup.py metadata was cleaned up. New hardware-free PyTorch tests cover the cross-frame attention helper, and CI now runs the full test suite rather than a single file.

dstack 0.20.22 expands Tenstorrent hardware support from Wormhole to Blackhole — PCIe cards, LoudBox, QuietBox, and Galaxy systems are now first-class dstack targets. The Vast.ai backend gains fine-grained offer filtering (min reliability, min score, offer ordering), a new Miles reinforcement learning example shows 32B GRPO training across a multi-node cluster, and AWS P3/V100 support is sunset.

tt-forge 1.2.0 ships the latest model uplifts from the forge models repository alongside a suite of new Tenstorrent ecosystem skills for automated Claude-based workflow testing. The release continues rapid iteration on CI automation, with AI-driven installation verification and an expanding test matrix against the latest model weights.

ttas v0.1.0 reaches a significant milestone: all 128 Wormhole b0 instructions are now fully cross-checked against Tenstorrent's canonical TT_OP_ macros in ckernel_ops.h, verifying opcodes, start bits, and field widths across the complete ISA. There's one breaking change — positional operand order in .tts files now follows the TT_OP_ macro signature rather than ascending start_bit order; named operands are unaffected.

ttnn-visualizer v0.87.0 improves the report loading experience with richer progress feedback during upload and remote sync, and now surfaces stack trace file contents stored in the profiler database directly in the UI. L1 Small tensors get their own filter and display path in the tensor list, and empty NPE traces are now rejected with a clear error rather than silently loading as broken reports.

ttas v0.0.1 is the first public release of a community-built assembler and disassembler for Tensix, the compute engine in Tenstorrent accelerators. It covers all 128 Wormhole b0 instructions generated from tt-llk's assembly.yaml, assembles .tts text into 32-bit Tensix words (binary, hex, or annotated C arrays), and shares a single instruction table between assemble and disassemble so the two paths can never drift apart.

tt-toplike v0.6.0 upgrades the Insights screen with process management capabilities and power analysis, giving operators a clearer view of what's running on their Tenstorrent hardware and how much power it's drawing — all from the familiar terminal monitoring interface.

A landmark release: ttsim v1.7.0 marks the initial public, open-source release of the Wormhole and Blackhole chip simulators, letting developers write and validate Tensix kernels, debug firmware, and explore hardware behavior without physical silicon. The release includes numerous QSR and Tensix bug fixes, improved error reporting, and optimized build flags — a powerful new resource for the broader Tenstorrent developer community.

tt-local-generator v0.3.3 adds AnimateDiff video generation to the local generation toolkit alongside broader artgen UX improvements. Running fully on Tenstorrent hardware, this expands the creative possibilities for developers experimenting with on-device generative AI beyond still images.

ttsim v1.6.4 adds ARC Control Store Memory (CSM) support on Wormhole to the simulator, making more of the hardware's debug and diagnostic path accessible in simulation. RISCV_DEBUG_REG_DBG_FEATURE_DISABLE is now readable (not just writable), and a round of Tensix bug fixes and improved error reporting further refines simulation accuracy.

dstack 0.20.21 adds JarvisLabs as a new cloud backend (including spot GPU instances) and brings multi-cluster Kubernetes support, where a single backend config can manage multiple clusters via kubeconfig contexts — each becoming its own dstack region with independent proxy and namespace settings. A handy update for teams running Tenstorrent workloads across heterogeneous cloud infrastructure.

TT Studio gains a Voice Agent wizard for multi-chip solution deployments, walking users through device allocation, model compatibility checks, and slot conflict resolution across multi-device setups. The release also ships an in-app bug reporting tool that automatically gathers service and runtime logs — making it easier to submit useful diagnostics without leaving the UI.

ttnn-visualizer v0.86.0 adds interactive I/O graph node highlighting when a focused operation is selected, refreshes the memory legend iconography for improved clarity, and optimizes the JavaScript bundle for the hosted deployment. Out-of-bounds memory items and tensors are now dimmed when an L1 memory range is set, and the project bumps to Node v24 LTS to restore Dependabot support.

A small but real papercut in the AnimateDiff Phase 2 walkthrough is fixed. The 16-frame run template was a single-quoted string, so a prompt like “World's Fair” needed an awkward backslash escape. Switching to a template literal lets the apostrophe reach the shell cleanly, so the example works exactly as written.

TT-Deploy follow-up: Blackhole Galaxy & SuperCluster, Networked AI architecture, industry-leading AI video & LLM inference benchmarks, and Day 0 model bring-up using Agent coding. Jun 15, 2026, Tokyo & online. — TT-Deployのフォローアップ。Blackhole Galaxyサーバー・SuperCluster、Networked AIアーキテクチャ、業界最速クラスのAI Video生成・LLM推論ベンチマーク、Agent codingを活用したDay 0モデル実装まで解説。2026年6月15日、東京オフィス＆オンラインのハイブリッド開催。

tt-inference-server v0.14.0 expands Blackhole QuietBox 2 (p300x2) model coverage significantly, adding FLUX.1-dev image generation, Wan2.2-T2V-A14B video generation, Llama-3.1-8B, Qwen3-32B, and Qwen3-VL-32B-Instruct. The CLI's run.py now clearly indicates which release version to use for models not yet uplifted since v0.10.0, making version navigation easier for operators managing multi-model deployments.

tt-flash v3.7.0 ships a self-contained PyInstaller binary as a new release artifact, making it possible to run the firmware flash tool on systems without a Python environment. As part of this transition, Debian package builds are sunset — the binary is the new recommended installation path for systems where pip isn't available.

dstack 0.20.20 adds support for NVIDIA Dynamo prefill-decode (PD) disaggregated inference, allowing replica groups to declare a Dynamo router and use SGLang, vLLM, or TensorRT-LLM as the inference backend — a powerful configuration for teams running large-scale inference on mixed hardware including Tenstorrent systems.

tt-smi v5.2.0 updates the UMD dependency from 0.9.4 to 0.9.5, picking up the latest hardware interface improvements and bug fixes from the underlying Tenstorrent device management library.

Kernels written by and for tt-lang are loaded to Blackhole chips on the Tenstorrent Quietbox 2, powering all forms of "AI" in the open source Civilization clone, FreeCiv. Look at all those fish!

Tenstorrent Low-Level Kernels: the C++ library that directly programs the RISC-V cores inside each Tensix compute engine. TRISC0 (unpack), TRISC1 (math/FPU/SFPU), and TRISC2 (pack) are all programmed through this layer — it is the interface between TT-Metal kernel code and bare silicon.

Lecture 20 from William & Mary's graduate Computer Architecture course. Frames Tenstorrent in the landscape between GPUs and TPUs, draws comparisons to Cerebras and SambaNova, then dives deep into the Wormhole chip and Tensix core: the 5 RISC-V core design, SFPU, NoC, and dataflow execution model.

A fused kernel for the Grayskull architecture implementing Transformer self-attention entirely within SRAM. Combines matrix multiply, attention score scaling, and Softmax without DRAM accesses, achieving significant speedups over non-fused implementations.

Evaluates the Tenstorrent Grayskull e75 RISC-V accelerator for matrix multiplication at reduced numerical precision (BFP8 and LoFi), a fundamental kernel in LLM inference computation.

Evaluates three strategies for scaling an N-body code across multiple Tenstorrent Wormhole accelerators. Builds on the established performance of single-card N-body work to explore parallelism via the on-chip NoC and multi-accelerator configurations.

Compiler system that automatically generates efficient dataflow plans for tile-based languages on spatial accelerators including Tenstorrent Wormhole. Exploits on-chip network forwarding between processing elements to reduce DRAM pressure.

Shows that Text-to-Speech inference on Tenstorrent Lightning V2 achieves 4× lower cost than NVIDIA L40S. Applies BlockFloat8 (BFP8) and low-fidelity (LoFi) precision strategies to TTS despite their greater numerical fragility compared to LLMs.

A 6,500-word community deep dive into the Blackhole p100a architecture: the tile model (Tensix, DRAM, SiFive x280 L2CPU, Ethernet, PCIe, NoC arc), firmware startup sequence, MOP micro-op processor, replay buffer, FPU/SFPU sync, and the anatomy of a kernel. From the author of blackhole-py.

ttnn-visualizer v0.85.0 introduces an experimental MLIR graph view in dev mode for inspecting compiler intermediate representations — a powerful new tool for compiler developers. Operation graph navigation now preserves focus context when moving to input/output operations, a scroll-to-tensor feature brings the Buffers view directly to the selected tensor, and database queries switch to explicit column names for better schema resilience.

tt-exalens v0.3.19 bumps UMD to 0.9.5 and adds a SIGBUS recovery mechanism in UmdDevice and UmdApi — an important reliability improvement for hardware debug sessions where PCIe access errors can otherwise crash the tool rather than being handled gracefully.

Maps 2D 5-point stencil computations onto the Tenstorrent Wormhole RISC-V AI dataflow accelerator via two implementations: element-wise decomposition (Axpy) and matrix-multiplication reformulation (MatMul). Profiling shows the isolated Wormhole kernel is competitive with CPU execution, with PCIe transfers and initialization driving end-to-end overhead; Axpy achieves lower energy than the CPU baseline at large scales. Identifies architectural and software directions for making AI accelerators viable for HPC stencil workloads. 2025.

tt-smi v5.1.1 is a quick follow-up that fixes a missing CSS file in the PyInstaller standalone binary, restoring the proper visual appearance of the system management interface when running as a self-contained executable.

New release: whisper 1.861

tt-exalens v0.3.18 adds performance counter debug registers for deeper hardware profiling, fixes a regex pattern issue in ICCM ID extraction, and resolves a display bug in the process info view — solid improvements for firmware and hardware debugging workflows.

Three interface fixes land here. The first-install theme logic now respects a theme set at workspace or workspace-folder scope, not just the global one, so it won't overwrite a deliberate per-project choice. The Tensix visualizer's play button again responds to labelled controls after a duplicate comparison was replaced with a prefix check, and the cluster visualizer's dot-mode animation no longer desyncs its column count from initialization. The latter two fixes were also backported upstream.

tt-smi v5.1.0 adds GDDR firmware version to the firmware table for more complete hardware visibility, ships PyInstaller binaries as official release artifacts, fixes a hang when using the eth_train_skip option with the UMD backend, and refactors the reset parsing logic into its own module for better maintainability.

tt-sim v1.0 is the first tagged release of a community-built Tenstorrent hardware simulator, providing a simulation environment compatible with the mesham/tt-metal fork. This gives developers an accessible way to explore tt-metal on Tenstorrent architecture without requiring physical hardware.

Fast full-system simulator of Tenstorrent Wormhole and Blackhole hardware. Runs TT-Metalium workloads on any Linux/x86_64 system without physical silicon. Bit-exact results relative to hardware.

PJRT device plugin for Tenstorrent hardware. Enables JAX, PyTorch/XLA, and other XLA-based frameworks to target TT accelerators.

Production-ready model serving for Tenstorrent hardware with OpenAI-compatible REST API. Supports continuous batching, multiple models, and all TT hardware configurations.

Python-based DSL that sits between TT-NN and TT-Metalium — expresses custom fused kernels with progressive disclosure, compiling directly to Tensix. Ships an integrated functional simulator (no hardware needed), line-by-line performance metrics, and AI-agent-friendly tooling. Two packages: tt-lang (compiler + hardware, requires ttnn) and tt-lang-sim (simulator only, works on Linux/macOS without Tenstorrent hardware).

Install the complete Tenstorrent software stack with one command. Handles drivers, firmware, Python environment, and SDK setup automatically.

48 interactive lessons covering the full Tenstorrent developer path — from hardware detection to custom training — with click-to-run commands and hardware auto-detection. Available in VSCode and code-server.

48 interactive lessons covering the full Tenstorrent developer path — from hardware detection to custom training — with click-to-run commands and hardware auto-detection. Available in VSCode and code-server.

Boltz-2 biomolecular model for drug discovery on Tenstorrent Blackhole. Supports single-card and multi-card configurations — QuietBox (4×) and Galaxy (32×). Approaches physics-based FEP accuracy at 1000× the speed.

A Tenstorrent-powered claw machine that rewards players with real prizes. The QuietBox 2 runs local AI inference to act as an agent controlling the claw hardware — the OpenClaw AI assistant lesson builds directly on this project.

Three agentic projects running fully on-device: local AI agents on QuietBox 2, a coding assistant powered by Aider against a local inference server, and the OpenClaw AI assistant on QuietBox 2. No cloud APIs — all inference runs on TT hardware.

Three agentic projects running fully on-device: local AI agents on QuietBox 2, a coding assistant powered by Aider against a local inference server, and the OpenClaw AI assistant on QuietBox 2. No cloud APIs — all inference runs on TT hardware.

On-device image generation with Stable Diffusion XL running entirely on Tenstorrent hardware. Full inference pipeline with no cloud dependency.

Three lesson-projects covering on-device video synthesis: frame-by-frame diffusion with tt-local-generator, native AnimateDiff video animation, and video generation on QuietBox 2. All run entirely on TT hardware with no cloud dependency.

Three lesson-projects covering on-device video synthesis: frame-by-frame diffusion with tt-local-generator, native AnimateDiff video animation, and video generation on QuietBox 2. All run entirely on TT hardware with no cloud dependency.

End-to-end image classification project using TT-Forge — compile and run a PyTorch classification model on Tenstorrent hardware with no kernel authoring required.

Interactive browser-based visualizer of the Tenstorrent Tensix grid architecture. Explore the NoC, core layout, and dataflow patterns without hardware — a great companion for learning kernel programming.

TT-Metalium implementation of Conway's Game of Life as a cookbook recipe. Each generation is a full parallel kernel dispatch over the grid — a clean introduction to stateful compute on Tensix cores.

Particle Life simulation on Tenstorrent hardware — an emergent-behavior N-body system where simple attraction/repulsion rules between species produce complex lifelike patterns. Cookbook recipe demonstrating parallel N-body compute on Tensix.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Seven-module computer science curriculum taught on real Tenstorrent hardware. Covers RISC-V architecture, memory hierarchy, parallel computing, networks and NoC, synchronization, abstraction layers, and computational complexity — all grounded in what is physically happening on the chip.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Eight-lesson series covering the full custom training workflow on TT hardware: dataset fundamentals, configuration patterns, fine-tuning, multi-device distributed training, experiment tracking, model architecture basics, and training from scratch.

Three hands-on TT-Metalium kernel recipes: a Mandelbrot fractal explorer, real-time audio signal processing pipeline, and custom image filter stack. Each recipe is a complete kernel project with full source in the lesson.

Three hands-on TT-Metalium kernel recipes: a Mandelbrot fractal explorer, real-time audio signal processing pipeline, and custom image filter stack. Each recipe is a complete kernel project with full source in the lesson.

Three hands-on TT-Metalium kernel recipes: a Mandelbrot fractal explorer, real-time audio signal processing pipeline, and custom image filter stack. Each recipe is a complete kernel project with full source in the lesson.

Three hands-on TT-Metalium kernel recipes: a Mandelbrot fractal explorer, real-time audio signal processing pipeline, and custom image filter stack. Each recipe is a complete kernel project with full source in the lesson.

Step-by-step guide to getting a Tenstorrent card running on Arch Linux with the full Metalium stack. Practical troubleshooting from someone who did it the hard way first.

Honest field notes from getting a Grayskull card running and writing first Metalium kernels. Covers setup pitfalls, processor hangs, memory protection quirks, and what makes Metalium compelling despite early rough edges.

Deep-dive into the Tenstorrent architecture and Metalium programming model — circular buffers, kernel synchronization, NoC routing, and where the footguns are. The honest guide to thinking in Tensix.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.

Ports the Cooley-Tukey FFT algorithm to the Wormhole n300 RISC-V accelerator. The Wormhole draws 8× less power and consumes 2.8× less energy than a 24-core Xeon Platinum for a 2D FFT. ISC 2025.

Ports the Cooley-Tukey FFT algorithm to the Wormhole n300 RISC-V accelerator. The Wormhole draws 8× less power and consumes 2.8× less energy than a 24-core Xeon Platinum for a 2D FFT. ISC 2025.

Accelerates an astrophysical N-body simulation on the Wormhole n300. Achieves 2× speedup and 2× energy savings over a highly optimized CPU implementation. SC '25 Workshop.

Accelerates an astrophysical N-body simulation on the Wormhole n300. Achieves 2× speedup and 2× energy savings over a highly optimized CPU implementation. SC '25 Workshop.

Implements three numerical kernels and composes them into a conjugate gradient solver on Wormhole. Demonstrates AI accelerators merit consideration for HPC workloads traditionally dominated by CPUs and GPUs. 2026.

Explores stencil computation on the Grayskull PCIe RISC-V accelerator. Early academic work examining TT hardware for HPC stencil workloads. 2024.

Makes multi-tenant NPU sharing practical for Blackhole-class hardware using polynomial-time allocation algorithms. Delivers up to 1.37× higher utilization and 1.14× faster workload completion. Up to 890,000× faster than NP-hard baselines.

tt-local-generator v0.2.6 adds SkyReels Image-to-Video (I2V) generation, expanding the tool's creative capabilities beyond text-to-image. The release also delivers artgen UX improvements and a website refresh, rounding out a growing local AI media generation toolkit that runs entirely on Tenstorrent hardware.

ttnn-visualizer v0.84.1 fixes a crash that could occur when checking configuration paths after a report had been deleted, and pins mypy to a known-good version to keep the type-checking pipeline stable.

TT-Deploy, May 1st, 2026 – Customers running Tenstorrent in production — David Bennett (AI&), Alex Nataros (Cirrascale), Sanchayan Sinha (Turiyam), and Mike Gorbinski (Virtu Financial) — share why they chose our hardware and what they're building on it.

TT-Deploy, May 1st, 2026 – Jim Keller reads our love letter to DeepSeek v4, reviews how Tenstorrent achieves unlimited scale, and shows off our video gen benchmark on Artificial Analysis. He closes with Tenstorrent is where AI runs.

TT-Deploy, May 1st, 2026 – Stan Sokorac, Sr. Fellow, Software, reveals the results of an agentic pipeline that continuously tests random Hugging Face models on Tenstorrent hardware: a 90% pass rate, projecting to roughly 2.5 million models. Plus a deep dive on TT-Lang, TT-Forge, and the only 100% open-source high-performance AI software stack.

TT-Deploy, May 1st, 2026 – Tenstorrent's Amr Elashmawi sits down with Justen Aguillon (Equinix), Sheng Yeo (OrionVM), and Abhishek Bhargava (BetterBrain) to walk through the Equinix Distributed AI Hub — a full-stack sovereign agentic AI platform now live in Ashburn.

TT-Deploy, May 1st, 2026 – Jasmina Vasilović, Senior Fellow of ML Frameworks & Programming Models, walks through the Tenstorrent software stack and how Blackhole's switch-free architecture extends the low-cost serving curve where GPU economics collapse — plus a partner spotlight on Prodia's faster-than-real-time WAN 2.2 video generation.

TT-Deploy, May 1st, 2026 – Jim Keller walks through the fundamentals – scale, general-purpose, and lower cost – that enable Tenstorrent to be built for the constant changing landscape of AI.

Join as we unveil Tenstorrent’s AI solutions deployed at scale. See the full breadth of what we've built — validated by real architecture, benchmarks, and customer deployments.

A release-pipeline fix corrects how the Open VSX publish step is gated. GitHub Actions doesn't expose the secrets context in step-level if: conditions, so the original check never behaved as intended. The step now tests env.OVSX_PAT — mapped from the secret in the same step — matching the pattern already used for the Marketplace publish job, so conditional publishing works reliably.

This release leans into hardening the lesson webview. External-link handling now opens only http and https URIs, ignoring file:, vscode:, command: and other schemes so webview messages can't trigger unintended actions, and YouTube embeds are converted to thumbnails before HTML sanitization runs, with the layout styles preserved through the sanitizer. Rounding it out are a click handler that guards against non-element targets, a broadened SVG regex so the agent sun-bleed effect fires reliably, and a stray bracket removed from a user-facing context-limit message.

dstack 0.20.19 adds a configurable time window for autoscaling RPS calculation (30s, 1m, or 5m), along with Kubernetes support for private registry authentication via imagePullSecrets and read-only volume mounts — giving teams running Tenstorrent inference servers more control over scaling behavior and deployment security.

All these videos were generated at home on Tenstorrent TT-QuietBox 2 using WAN 2.2 and SkyReels.

Using Qwen3-32B is used in three inter-connected agent demos. Learn how on your own: https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/qb2-local-agents/ Visualizer is tt-toplike: https://docs.tenstorrent.com/tt-toplike Time is condensed in multiple segments.

tt-toplike v0.5.0 fixes border and centering issues for consistent layout across terminal sizes and introduces a --mock N shorthand for quickly launching a mocked view with N simulated devices — handy for development and demonstrations without requiring physical Tenstorrent hardware.

tt-local-generator v0.2.2 refreshes the homepage and expands the built-in prompting bank, making it easier to discover and use the range of generation capabilities available through this local AI toolkit running on Tenstorrent hardware.

tt-local-generator v0.2.1 fixes environment variable handling in the Debian package configuration, ensuring the installer correctly reads and applies user-provided settings on first install.

tt-toplike v0.4.3 refines the CI build pipeline with improved dependency handling for Debian package builds, smoothing the release process for this terminal monitoring tool for Tenstorrent hardware.

tt-toplike v0.4.2 establishes proper GitHub Actions release and CI workflows, putting this terminal hardware monitor on a solid automated foundation for consistent, tested releases going forward.

tt-inference-server v0.13.0 marks the first release of Forge-based models — ResNet-50, VoVNet, MobileNetV2, SegFormer, and ViT — powered by the newly released tt-forge library 1.0.0. Multiple models graduate from EXPERIMENTAL to FUNCTIONAL or COMPLETE status on N150/N300, expanding the range of production-ready computer vision workloads on Wormhole hardware.

tt-exalens v0.3.17 is a targeted fix that corrects the send_tensix_risc_reset function's keyword argument to match current UMD bindings, restoring RISC reset functionality that had broken with recent UMD API changes.

tt-smi v5.0.1 adds a GitHub Actions workflow for building PyInstaller standalone binaries, laying the foundation for distributing tt-smi as a self-contained executable that works on machines without a full Python environment.

Each chip is compiling independently from the others. Some time manipulation to fit within a minute, this covers about 14 minutes of compilation. Find the tt-forge-compiletron here: https://github.com/tsingletaryTT/tt-forge-compiletron

tt-exalens v0.3.16 resolves intermittent CI failures caused by an underlying UMD bug, restoring reliable test runs and keeping the development pipeline green.

Using Qwen3-32B is used in three inter-connected agent demos. Learn how on your own: https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/qb2-local-agents/ Visualizer is tt-toplike: https://docs.tenstorrent.com/tt-toplike

TT System Firmware v19.9.0 delivers significant power and performance improvements for Wormhole: DRAM low power mode gets targeted fixes for slow-retraining instances, Tensix RISCs are now properly reset before entering clock-gating state, and PCIe low power mode is temporarily disabled while unresolved issues are addressed. Fan telemetry gains RPM reporting and a corrected speed percentage, and Blackhole gains support for reading VDD_MIN/VDD_MAX from the firmware table.

TT Studio v2.5.0 is a major capability release: the model catalog now syncs from the TT Inference Server artifact and covers LLM, TTS, STT, and VLM model types with per-device compatibility indicators. A new ChipSlotAllocator handles automatic chip assignment across deployments, a pulsing progress bar replaces the static deployment wait screen, and a dedicated Docker Control Service improves security by replacing direct socket access — with deployment timeouts extended to 5 hours for large model downloads.

tt-flash v3.6.5 fixes the reset path for revision C Galaxy systems, ensuring IPMI reset is always used — an important correction for safe firmware flashing on the latest Galaxy hardware.

This release upgrades to Linux Kernel 7.0 and adds device tree support for performance monitoring, letting you track system metrics directly from userspace. The kernel now ships with a patch that eliminates spurious swiotlb errors during boot, cleaning up initialization logs and making debugging easier. Several fixes to the console tool and build system round out the update.

tt-bh-linux v0.11 updates to Kernel 7.0 and adds device tree nodes for Linux performance monitoring, enabling perf tooling on Blackhole hardware. A kernel patch eliminates swiotlb initialization errors that appeared on boot, and the console tool and Makefile receive miscellaneous fixes.

tt-flash v3.6.4 wraps IPMI reset operations with the required USER_RESET ioctl before and POST_RESET ioctl after, ensuring the complete reset sequence is honored — a prerequisite for safe firmware flashing on Galaxy systems alongside the new tt-kmd 2.8.0 driver.

tt-flash v3.6.3 reorganizes Wormhole-specific flash logic into its own module, adds the ability to download firmware directly from tt-system-firmware GitHub release assets with local caching, and introduces a guard that prevents P300 flashing unless both chips are detected — avoiding partial flash scenarios that could leave a dual-chip card in an inconsistent state.

tt-inference-server v0.10.1 is a targeted reliability fix: a deadlock in the media server that could hang inference under certain conditions is resolved, and the trace region size for Llama-3.1-70B models is increased to prevent trace buffer exhaustion during long-running sessions.

The PCIe kernel driver gains stable, topology-derived device ordinals for Galaxy systems, replacing probe-order numbering that could vary across reboots. Hotplug processing is suppressed on Galaxy to prevent PCIe link speed degradation, and a DMA bug affecting Thunderbolt-connected devices is fixed — Galaxy users should also upgrade to tt-smi v5.0.0 and tt-flash v3.6.4 alongside this driver.

tt-inference-server v0.12.0 adds experimental support for DeepSeek-R1-0528 and OpenAI's gpt-oss-120b on Wormhole Galaxy and Dual-Galaxy configurations, establishing early foundations for running frontier-scale models on Tenstorrent hardware. Both carry experimental status (R1-0528 can hang during decode; gpt-oss-120b occasionally produces incomplete special tokens), alongside updated multi-host documentation.

tt-smi v5.0.0 implements the full Galaxy reset sequence (USER_RESET before IPMI reset, POST_RESET after) required by the new tt-kmd 2.8.0 driver — Galaxy users should upgrade to this version alongside the kernel driver update. The frontend formatting logic is refactored into its own module, and legacy glx_reset_tray support is removed.

luwen v0.8.5 adds support for the PCIE_LINK_SPEED power flag and gracefully handles EINVAL errors from the set_power_state ioctl when running against older firmware that doesn't support the new power state interface — ensuring backward compatibility with older firmware versions across the Tenstorrent hardware stack.

luwen v0.8.3 delivers a complete overhaul of the CI pipeline, establishing a robust automated testing foundation for this Rust-based hardware abstraction library that underpins much of the Tenstorrent software stack.

TT Studio v2.4.1 replaces the tt-inference-server submodule with a cleaner artifact-based integration supporting configurable branches and release versions with automatic re-download on change. Time to First Token (TTFT) and Time Per Output Token (TPOT) metrics arrive for live performance monitoring during inference streaming, alongside a dedicated Docker Control Service that replaces direct socket access with a more secure abstraction.

luwen v0.8.2 fixes a sign error in 16.16 fixed-point temperature decoding that could produce incorrect readings for cold hardware, adds Blackhole support for boards with multiple bootfs headers, and skips unnecessary DRAM initialization waiting on Wormhole — alongside several board ID debug and flash diagnostic utilities.

Multi-device rigs heat up evenly now: the power ioctl is sent to all devices instead of just the first (#28), so a burn-in run genuinely exercises every card in the box. The tool also stops forcing RUST_BACKTRACE=full (#29), leaving backtrace verbosity to you.

Galaxy systems and TT-Fabric inter-device communication: scalable compute from Wormhole/Blackhole through Galaxy to SuperCluster, with software and network architecture deep-dives. Apr 20, 2026, Tokyo & online. — GalaxyシステムとTT-Fabricによるデバイス間通信を解説。Wormhole/BlackholeからGalaxy・SuperClusterへのスケールアウト計算基盤と、それを支えるソフトウェア・通信アーキテクチャを詳しく紹介。2026年4月20日、東京オフィス＆オンラインのハイブリッド開催。

tt-umd v0.9.4 delivers focused infrastructure improvements: TopologyDiscoveryOptions is refactored for cleaner configuration, Ethernet link retraining on 6U chassis gets dedicated support, DRAM retrain capability is added, and the DeviceProtocol interface evolves to better accommodate the TTSim path. Available as DEB/RPM packages for Ubuntu 22.04/24.04, Fedora 39, and Python wheels for Python 3.9–3.13.

tt-installer v2.2.1 updates the firmware download URL to the new endpoint, ensuring the one-line installer correctly fetches the latest firmware during fresh Tenstorrent hardware setup.

tt-installer v2.2.0 adds optional Forge container installation to the setup flow — now you can set up a complete tt-forge development environment as part of the initial hardware installation process, with the Forge container defaulting to off for users who don't need it.

This release fixes a critical SIGBUS crash that could occur after device reset by reordering the chip re-detection sequence—a subtle but important ordering issue that was causing memory access violations. If you've encountered hangs or crashes when resetting devices in your workflows, this update should resolve those stability problems.

This release continues hardening the UMD stack with improved stability and performance across multiple fronts. Key changes include moving TLB configuration back to userspace to eliminate kernel-mediated performance penalties, implementing full SPI support for both Wormhole and Blackhole architectures, and fixing a subtle hash collision bug in coordinate pairs that was causing kernel cache corruption. The team also shipped safer device read/write APIs to protect against SIGBUS errors, updated device filtering to use stable logical IDs instead of volatile PCI IDs, and resolved numerous static analysis warnings to improve code quality. Python wheels now support Python 3.9–3.13 on modern Linux distributions, and system packages are available for Ubuntu 22.04/24.04 and Fedora 39.

This release brings memory and stability improvements across Tenstorrent's platform lineup, with Wormhole now supporting Samsung GDDR6 and both platforms implementing security wipes during initialization. On Blackhole, the p300 gains critical fixes for reboot reliability and expanded board support with Galaxy revC, while p300c power limits have been tuned (550W board power, 125W TDP, 88°C GDDR thermals). The telemetry layer also gets new visibility into AICLK arbitration decisions with granular tracing, helping developers debug frequency scaling behavior more effectively.

This release brings the Linux stack for Grayskull and Wormhole to kernel 6.19 with OpenSBI 1.7, leveraging upstreamed device tree support that's now part of the mainline kernel. The update improves compatibility with newer kernel mode driver and firmware versions, particularly around power management capabilities, and adds proper multi-card system support—a key requirement for deployments like quietbox that span multiple accelerators.

The PCIe driver now automatically invalidates userspace memory mappings during device resets, ensuring applications see faults rather than reaching undefined device state—though this means you'll need to close and reopen device files after a reset if your code was relying on the previous behavior. New features include blocking lock acquisition for multi-process coordination, standardized ERISC lock indices, and sysfs telemetry for firmware heartbeat and thermal shutdown events. The release also patches a kernel quirk affecting Blackhole Galaxy link speeds on Linux 6.5–6.12 and fixes several race conditions in device removal, secondary bus resets, and suspend/resume handling.

This patch release fixes snapshot mode to generate valid JSON output, addressing a parsing issue that would have affected downstream tooling. The update also introduces loop snapshot mode for continuous monitoring and extends snapshot data to capture process-level details and encode/decode statistics, giving users more granular visibility into system activity.

The Zephyr Platforms firmware v19.5.0 brings stability improvements across Wormhole and Blackhole platforms, alongside a significant change to Blackhole's core configuration. Starting with this release, all Blackhole p150 cards now report 120 Tensix cores instead of 140, unifying the interface across software layers—expect roughly 1–2% performance variance and potential grid layout changes in metal. On the connectivity side, Wormhole's ERISC firmware gets refined link training and signal integrity adjustments, while Blackhole gains Manual EQ training, improved SerDes initialization sequencing, and expanded telemetry for clock arbitration and power monitoring. Be sure to upgrade tt-flash to 3.6.0 or later to avoid losing board IDs on Blackhole during the update.

This release introduces mesh_v2 layout support and multi-host programming capabilities to tt-topology, enabling developers to work with expanded device configurations across multiple hosts. The changes include coordinate mapping adjustments for the new layout option and standardized constant formatting to align with the rest of the codebase, making it easier to integrate multi-host setups into existing Tenstorrent workflows.

This release aligns tt-topology with the latest SMI (System Management Interface) compatibility by bumping pyluwen and tools-common dependencies to version 72. The update ensures consistent versioning across the Tenstorrent toolchain, which helps maintain stability when coordinating with other system management components. These are primarily maintenance changes that keep the topology layer in sync with broader platform improvements.

Tenstorrent's RISC-V CPU TT-Ascalon: a deep dive by the engineers building it, covering the TT-Blueprint announcement and the new OSS Developer Program. Feb 26, 2026, Tokyo & online. — Tenstorrentが注力するRISC-V CPU「TT-Ascalon」を開発エンジニア自らが解説。TT-Blueprintで発表されたHigh performance RISC-V CPUの詳細と、新たにスタートするOSS Developer Programについても紹介。2026年2月26日、東京オフィス＆オンラインのハイブリッド開催。

UMD v0.9.1 is now available on PyPI, making it simpler to install the Python bindings across Python 3.9–3.13 on modern Linux distributions. Beyond the PyPI publication, this release includes fixes for 6U device reset performance (reducing warm reset time from ~6 minutes to ~1 minute), improved ARC core startup reliability with better timeout handling, and a multiprocessing test to validate shared lock behavior across forked processes. Compiled artifacts, system packages for Ubuntu 22.04/24.04 and Fedora 39, and updated topology discovery logic round out the update.

This bugfix release corrects a memory reporting issue that was causing inaccurate available device memory readouts on NVIDIA GPUs, and folds effective utilization metrics directly into the GPU utilization display so you get a clearer picture of actual workload efficiency without extra navigation.

Syllo/nvtop 3.3.0 expands hardware coverage to Rockchip NPUs, MetaX GPUs, and Enflame GCUs while refining monitoring capabilities across existing platforms. The release introduces an effective load metric that weights utilization by power consumption, separates GPU and memory clock reporting, and improves one-shot mode output—alongside numerous fixes for Mali, Intel Battlemage, AMD integrated graphics, and Nvidia unified memory tracking. If you're monitoring accelerators beyond the usual suspects or need more granular power-aware metrics, this update brings meaningful improvements to the toolkit.

This release broadens installer compatibility and adds new installation options for more flexible Tenstorrent setups. The distro detection now handles derived Linux distributions like Linux Mint by checking the ID_LIKE field, while a new --use-uv flag offers faster Python package installation as an alternative to pip. You can now choose to install Docker as a container runtime option alongside existing choices, and a new "default mode" simplifies the tt-studio installation flow for users who want a streamlined setup experience.

This patch release restores backward compatibility for Blackhole's READ_{TS,PD,VM} message interfaces, reverting changes that shipped in v19.1.0 and would have broken existing code. Additionally, the team disabled MRISC PHY powerdown on BH Galaxy after discovering it caused data errors, prioritizing stability over the power savings the feature was intended to provide.

A packaging-focused cut: beyond the version bump, this build generates proper Ubuntu and Debian changelogs — it's the version now shipping as python3-tt-tools-common in the Tenstorrent PPA, so the shared plumbing under tt-smi, tt-flash, and friends installs cleanly via apt.

Chip resets get more configurable with a new secondary_bus_reset option in the reset sequence (#60) — useful when the default secondary-bus step doesn't suit your platform. The full_lds_reset path also picked up cleaner arguments and much more detailed docstrings, so anyone scripting resets against this library has a clearer contract to work with.

Mostly a foundation refresh: pyluwen moves to 0.8.0 and tt-tools-common to 1.5 (#26), with Python 3.10 now the floor. There's one behavioral nicety for Blackhole — the legacy GO_BUSY/GO_IDLE power messages are only used when tt-kmd is older than v2.6.0, deferring to the driver's native handling on current installs.

This patch reverts the Wormhole ERISC firmware to version 6.7.2.0 after 6.7.3.0 introduced ETH training instability in WH Galaxy systems. If you've been experiencing unreliable Ethernet initialization on Wormhole hardware, this update should restore the stability you had before.

The headline is a dependency step-up to pyluwen 0.8.0 (#61), pulling the newer Rust-backed device communication layer into every tool that builds on this library. Release automation also learned to take an explicit version-bump type (#62), which keeps future versioning deliberate rather than guessed.

This release focuses on streamlining the CI/CD pipeline for publishing, particularly around TestPyPI uploads and artifact handling. The changes ensure that the release process skips redundant artifact uploads, removes unnecessary source distributions during testing phases, and properly sequences the release generation step after TestPyPI publication. These refinements should make the release workflow more efficient and reduce friction for maintainers pushing updates to the ecosystem.

This release tackles stability across multiple fronts: Wormhole firmware gets link training fixes for active cables and improved signal integrity on long traces, while GDDR latency tuning and a Galaxy datarate rollback address recent regressions. On the driver side, SMBus transaction handling now properly respects cancel state, fixing PCIe enumeration issues that users were seeing. The BH ARC library adds cleaner power management tracking for AICLK state and better structured access to system messages, plus some developer conveniences like flash erase support in the bootstrap tool and a version update script to streamline releases.

New release: tt-system-tools v1.4.1

This release fixes multi-host topology configuration by removing a single-host assumption in the connection map generation, which should help users with distributed setups properly discover and configure their hardware. The board type detection has also been expanded to recognize newer Tenstorrent boards, and tt-tools-common has been bumped to 1.4.33 to pull in any upstream improvements.

This release significantly streamlines the installer by removing legacy installation paths and consolidating the codebase around repository-based package management. Ubuntu 20 support is now fully deprecated, backwards-compatible environment variables have been removed, and manual installation workflows have been phased out in favor of a cleaner argument-parsing approach. The changes also add distribution-aware package manager detection and improve handling of Debian systems specifically, while fixing various shell script issues and ensuring the inference server is properly invoked during installation.

A reset-timing bug is fixed: the wait duration is now actually taken from the function parameter instead of being ignored (#59), so callers that tune reset waits get the behavior they asked for. A stray height style was also dropped from the host-compatibility menu (#58), tidying the TUI shared by the system tools.

The loader and scheduler in RiescueD have been refactored with explicit interfaces that make their control flow clearer and reduce redundant checks—test_setup now runs once during scheduler initialization rather than on every loop iteration. Bug fixes address deterministic CSR ordering, privilege mode handling in virtualized environments, and stack allocation for algorithm tests, while new documentation with Mermaid flowcharts and API details should help developers understand and debug runtime behavior more easily.

This release expands tt-installer's reach and capabilities by adding Fedora package support through a hosted repository and introducing an install-inference-server function for streamlined deployment of inference workloads. If you're running Fedora or planning to set up inference services, these additions make the installation process more straightforward and reduce platform-specific friction.

Riescue now gives you fine-grained control over test execution with a dedicated End of Test (EOT) module and hooks that let you inject custom logic into the runtime environment. The new Conf library lets you handle complex configuration scenarios in Python while staying within the CLI workflow, making it easier to tailor test sequences without wrestling with command-line arguments. Updated documentation covers all three features, so you can start customizing your test lifecycle right away.

TTNN DNN library deep-dive: design, supported ops, memory/tensor layout, and hands-on porting from PyTorch to TTNN with optimization techniques. Dec 10, 2025, Tokyo & online. — TTNNの設計・命令群・Memory/Tensor Layoutを解説し、PytorchからTTNNへの移植と最適化テクニックを実践的に紹介。TT-CNNをベースとしたモデル実装例も取り上げます。2025年12月10日、東京オフィス＆オンラインのハイブリッド開催。

This release strengthens Riescue's reliability and usability with Hart-local storage in the RiescueD runtime that prevents register corruption from unexpected traps—a key fix for stable low-level execution. Documentation improvements now cover the RiescueD runtime environment and link directly to tensor parallelism tutorials, while mode-specific fixes in RiescueC and early standardization work on the test scheduler lay groundwork for more consistent interfaces across the stack.

This release updates tt-topology to handle Xenium reset schema changes via a tools-common bump, while adding better device detection robustness—the tool now exits gracefully when no supported devices are found rather than proceeding with incomplete configuration. A couple of test fixes round out the changes, keeping the codebase clean as the topology layer evolves alongside hardware updates.

Running Tenstorrent cards under Xen gets first-class treatment: the reset flow now detects Xen HVM guests, waits for the xenstore file to clear instead of writing blindly, caches sudo access, and catches Xen-specific exceptions (#45). If you're virtualizing your accelerators, resets should now behave instead of racing the hypervisor.

Thaddeus Fortenberry, VP of Robotics + Automotive at Tenstorrent, discusses how we're paving the open road to chiplet interoperability in robotics. Open Chiplet Atlas | https://www.openchipletatlas.org Tenstorrent Robotics IP | https://tenstorrent.com/ip X https://twitter.com/tenstorrent Discord https://discord.com/invite/tenstorrent

Watch Jim Keller and Miles Dooley kick off Tenstorrent's IP launch event in San Francisco, TT-Blueprint. Tenstorrent IP | https://tenstorrent.com/ip RISC-V CPU | https://tenstorrent.com/ip/risc-v-cpu Open Chiplet Atlas | https://www.openchipletatlas.org/ X https://twitter.com/tenstorrent Discord https://discord.com/invite/tenstorrent

Wei-Han Lien, Chief Architect at Tenstorrent, discusses how Tenstorrent is pushing the chiplet ecosystem forward with Open Chiplet Atlas. Open Chiplet Atlas | https://www.openchipletatlas.org/ Tenstorrent IP | https://tenstorrent.com/ip RISC-V CPU | https://tenstorrent.com/ip/risc-v-cpu X https://twitter.com/tenstorrent Discord https://discord.com/invite/tenstorrent

Aniket Saha, VP of Product Strategy at Tenstorrent, discusses Tenstorrent's new, open business model for high-performance IP and why the old, closed-ecosystem approach isn't working. Tenstorrent IP | https://tenstorrent.com/ip X https://twitter.com/tenstorrent Discord https://discord.com/invite/tenstorrent

Makunika-hosted Tenstorrent seminar in Osaka: product updates and user sessions from Kinki University, NTT DOCOMO Business, AICE, and Makunika on AI accelerator and LLM deployments. Nov 28, 2025, Osaka. — マクニカ社主催のTenstorrentセミナーが大阪で開催。Tenstorrent製品最新情報に加え、近畿大学・NTTドコモビジネス・AICE・マクニカ社によるユーザーセッションでLLM実行基盤やAIアクセラレータ活用事例を紹介。2025年11月28日、AP大阪駅前。

Riescue v1.1.2 adds better error handling for runtime internal failures with new loader_panic and trap_handler_panic mechanisms, making it easier to catch and debug issues when things go wrong during execution. The release also includes pyright type-checking fixes and documentation updates that should improve the developer experience overall.

This release brings the Blackhole platform up to Kernel 6.17 and OpenSBI 1.7, with the kernel patches now aligned with upstream RISC-V contributions. Note that the device tree filename has changed from blackhole-p100.dtb to blackhole-card.dtb, so you'll need to update any boot configurations accordingly. The userspace application has also been updated to handle the multi-descriptor packet format that virtio-net now uses in 6.17, ensuring network virtualization works smoothly on the updated stack.

tt-studio v2.1.0 brings meaningful improvements to agent capabilities and operational reliability, with automatic LLM discovery and health monitoring making multi-model setups more robust, plus a code interpreter tool expanding what agents can do. The release tackles several critical issues including a tt-smi blocking problem that was slowing backend execution, and adds new diagnostics endpoints for API information and service logs that help developers understand what's happening under the hood. UI polish comes through auto-refreshing health checks, expandable model details, and better dark mode support, while infrastructure upgrades to Node.js 22 and Vite 6.3.5 keep the foundation current. For teams running tt-studio in production, the health check improvements and process cleanup fixes should reduce operational friction considerably.

Blackhole AI accelerator overview (incl. RISC-V X280) and TT-Forge/tt-mlir MLIR-based compiler deep-dive, plus user lightning talks and a swag giveaway. Oct 24, 2025, Tokyo & online. — Tenstorrent最新世代Blackholeの概要（RISC-V X280含む）と、MLIRベースのコンパイラTT-Forge/tt-mlirの設計・実装を解説。ユーザーによるライトニングトークとスワグ抽選会も実施。2025年10月24日、東京オフィス＆オンラインのハイブリッド開催。

The virtio device implementation and X280 kernel interrupt handler get more robust with this update, addressing edge cases and reliability concerns identified in the codebase. If you're running workloads on Blackhole hardware, the improved PLIC handler should make interrupt handling more predictable, and the virtio changes reduce the surface area for device communication failures. Updated disk images and kernel binaries are ready to download if you want to test these improvements.

New release: tt-torch 0.4.0

New release: polaris pre_perfmodel_merge

Local LLM on Tenstorrent AI accelerators: TT-Studio, vLLM extension, Inference Server demos, and AI Agent integration with TT-Transformers and Cursor. Developer bounty program intro. Sep 11, 2025, Tokyo & online. — TenstorrentアクセラレータでのローカルLLM活用を解説。TT-Studio・vLLM拡張・Inference Serverのデモと、TT-TransformersとCursorを使ったAI Agent連携も紹介。US本社エンジニアも登壇。2025年9月11日、東京オフィス＆オンラインのハイブリッド開催。

The Debian image now includes ca-certificates, which fixes HTTPS connectivity issues that were blocking proper SSL/TLS verification in the environment. This is a foundational fix that ensures secure communication works as expected when building and deploying applications on the Blackhole platform.

This patch release polishes the user experience with a focus on visual clarity and accessibility—the team cleaned up a confusing close button in the UI and fixed error text that was hard to read in light mode, while also refining theme consistency across the application. The improvements reflect community feedback, including a first-time contribution that improved the overall interface clarity. If you've been working with Studio and noticed visual quirks switching between light and dark modes, this update should smooth out those rough edges.

The team has published a white paper on an automated approach to generating architectural coverage models from an instruction set simulator, addressing the challenge of verifying increasingly complex ISA extensions. This framework bridges ISS-only simulations and RTL co-simulation environments, allowing coverage sampling to scale alongside ISA growth without manual effort. For verification engineers, this means a systematic path to comprehensive coverage that adapts across different simulation contexts rather than reinventing verification methodology with each architecture iteration.

This session, led by Tapasvi Patel, Sr. Engineer at Tenstorrent, covers how Tenstorrent's compiler (TT-Forge) and runtime (TT-NN) manage device meshes, tensor sharding, and collective communications, with a focus on using JAX. Also, see how Shardy helps with automatic parallelization. 0:00 Introduction 0:25 Modeling multi-device in Tenstorrent's compiler & runtime 5:33 Parallelism strategies and multi-chip hardware 8:38 Core TT-NN runtime features for multi-device (meshes, tensors, mappers)…

Heterogeneous GPU infrastructures present a binary compatibility challenge: code compiled for one vendor's GPU will not run on another due to divergent instruction sets, execution models, and driver stacks . We propose hetGPU, a new system comprising a compiler, runtime, and abstraction layer that together enable a single GPU binary to execute on NVIDIA, AMD, Intel, and Tenstorrent hardware. The hetGPU compiler emits an architecture-agnostic GPU intermediate representation (IR) and inserts…

Wormhole/Tensix NoC data transfer and TT-Metalium kernel programming: memory architecture, inter-core dataflow, and hands-on kernel implementation on Tensix cores. Jul 16, 2025, Tokyo & online. — Wormhole/TensixアーキテクチャのNoC（Network-on-Chips）を使ったデータ転送の仕組みと、TT-Metaliumによるカーネルプログラミングを解説。メモリアーキテクチャとTensixCore間のデータフローを深掘りします。2025年7月16日、東京オフィス＆オンラインのハイブリッド開催。

With the rapid development of artificial intelligence (AI) applications, an emerging class of AI accelerators, termed Inter-core Connected Neural Processing Units (NPU), has been adopted in both cloud and edge computing environments, like Graphcore IPU, Tenstorrent, etc. Despite their innovative design, these NPUs often demand substantial hardware resources, leading to suboptimal resource utilization due to the imbalance of hardware requirements across various tasks. To address this issue,…

Inaugural Tenstorrent Tech Talk: HW/SW overview, Tensix-Core architecture deep-dive, and intro to the developer community and Bounty Program. Jun 6, 2025, Tokyo & online. — 第1回TenstorrentテックトークではTenstorrentのAI専用アクセラレータ全体像、ハードウェア構成とソフトウェアスタックの概要、「Tensix-Core」アーキテクチャを詳しく解説。開発者コミュニティとBounty Programも紹介。2025年6月6日、東京オフィス＆オンラインのハイブリッド開催。

TT-Forge is Tenstorrent’s MLIR-based compiler. This Q&A covers its architecture, front-end support (Torch, JAX), tools like TT-Explorer, and key discussions on runtime customization, debugging, production timelines, quantization, model training, the PiKernel DSL, and how to contribute. 0:00 What is TT-Forge? 1:09 Accessing Tenstorrent hardware on Koyeb 4:39 Compiler production timeline and manual model implementation 7:24 TT-Explorer for quantization schemes 8:26 Model training framework with…

Tenstorrent Developer Day Livestream April 3rd at 10 am PT

TT-Forge is Tenstorrent’s MLIR-based compiler. Learn how TT-Forge integrates with our AI software stack, why we’re building on MLIR, and the features that make TT-Forge flexible and adaptable. 0:00 Introduction 1:36 Integration with key ML frameworks 2:05 Why MLIR? 3:10 Driving principles of TT-Forge 7:48 Recap tt-forge: https://github.com/tenstorrent/tt-forge tt-mlir: https://github.com/tenstorrent/tt-mlir Follow Tenstorrent on X at https://x.com/tenstorrent Join our Discord at…

nvtop 3.2.0 expands hardware coverage significantly with support for Intel XE GPUs, Broadcom V3D accelerators (Raspberry Pi), and Google TPUs, making it a more versatile monitoring tool across different compute platforms. Beyond new hardware, the release adds practical features like JSON snapshot exports and a process-list hiding option for cleaner monitoring views, while improving existing Intel i915 support and backend stability. The conda-forge availability makes installation simpler for users in that ecosystem.

This release brings byte-alignment compliance testing to the RISC-V architecture test suite, ensuring that RV IMFA tests across all privilege levels (Machine, Supervisor, and User) and configurations (bare metal and paging disabled) properly enforce alignment constraints. The CI pipeline now validates that these tests correctly reject misaligned memory accesses, catching potential issues where implementations might incorrectly permit unaligned data or instruction fetches that the spec forbids.

This release cuts repository size by removing pre-0.1.1 history after the shift to source-based distribution, making the codebase easier to work with. The team also fixed a linker script copy issue that was affecting test builds and refined the test infrastructure. Going forward, they're planning automated releases and expanded test coverage to catch issues earlier in the validation pipeline.

The project has shifted to a more maintainable release structure: source files and linker scripts are now directly in the repository rather than bundled in compressed archives, making it easier for users to modify tests for their needs. A .tar.zip of pre-compiled ELFs built with the GNU toolchain is provided separately for those who want to use tests out of the box, streamlining the workflow for both custom modification and quick integration.

This release brings support for ten additional model variants—including Phi2, Qwen1.5-0.5B, and YOLOX—along with initial Wormhole n300 dual-chip support for the TT-LoudBox and TT-QuietBox systems. Performance improvements are mixed across architectures: Grayskull sees solid gains on HRNet (41%), while Wormhole n300 single-chip shows 14% uplift on Falcon-7B, though some models like FLAN-T5 have regressed. CNN models now work on 4-chip and 8-chip MMIO configurations, and the compiler received stability fixes alongside improved documentation.

New release: zyx v0.14.0

Quite pleasing to hear Jim admit he had wrong assumptions about how difficult this was going to be. I genuinely can't fathom a piece of hardware that can actually run tensor operations concurrently. Not sure how to design it or invent it. Cool idea though! https://m.youtube.com/watch?v=cy-9Jl666Aw&t=2s submitted by /u/ronalurker777 [link] [comments]

submitted by /u/brand_momentum [link] [comments]

submitted by /u/brand_momentum [link] [comments]

submitted by /u/brand_momentum [link] [comments]

This release expands model coverage with five new variants including SSD300 ResNet50 and multiple YOLOv6 configurations, while introducing initial support for Wormhole n300 hardware across single-chip and 4-chip setups. Performance gains are mixed—Wormhole n150 shows broad 6% improvements with standouts like MobileNetV2 (46%) and YOLOv5 (20%), though Grayskull sees some notable regressions that the team plans to address—alongside general compiler stability improvements and better documentation. Known issues around Whisper, Stable Diffusion, and ethernet hangs on dual-chip n300 data parallel configurations are flagged for upcoming patches.

https://arxiv.org/abs/2406.02528 submitted by /u/ronalurker777 [link] [comments]

This release brings meaningful performance gains across both Grayskull and Wormhole hardware, with Wormhole seeing particularly strong results (52% average improvement with no regressions) and Grayskull hitting 30% average gains, though a few models like FLAN-T5 saw slowdowns that may warrant investigation. On the feature side, there's new support for Perceiver IO and HarDNet architectures, Ubuntu 22.04 is now the default OS, and the compiler picked up various stability fixes including better handling of multi-user workloads—all of which should make the platform more robust for production deployments. DeBuda is now available as a standalone wheel, and documentation improvements should help new users get up to speed faster.

The latest release streamlines zyx's architecture by splitting the codebase into separate Cargo crates and cutting dependencies, while delivering meaningful performance gains and a cleaner API surface. New tools like the dedicated compiler crate and a redesigned backpropagation system (with tensor detaching for RNNs) make it easier to build and experiment with custom backends, especially since backends no longer need matmul implementations. The addition of SGD and Adam optimizers, tensor indexing, and an expanded set of operations rounds out a release that feels more focused on developer ergonomics than raw feature count.

nvtop now covers a broader range of accelerators with added support for Adreno GPUs via the panfrost driver, Apple Silicon GPUs (M1/M2), and Huawei Ascend accelerators, making it more useful across heterogeneous hardware environments. The release also fixes a crash related to configuration file discovery and prevents unnecessary handler calls for disabled devices, improving stability and efficiency for users managing diverse GPU setups.

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submitted by /u/JoesRevenge2 [link] [comments]

This release brings a PyTorch backend to zyx via the tch crate, letting you integrate the framework more directly with existing torch workflows. The update also adds operation counting to help with model profiling, and reorganizes the test suite for better maintainability—useful housekeeping as the project grows.

The CPU backend now treats movement operations as no-ops, eliminating unnecessary data shuffling during computation. This architectural change delivers measurable performance gains by removing overhead that was previously wasted on operations like transposes and reshapes that don't need to actually move data around in memory.