tt-awesome — Recent Releases Latest stable releases from Tenstorrent ecosystem projects https://tenstorrent.github.io/tt-awesome/feeds/releases.xml Tenstorrent Communityhttps://tenstorrent.github.io/tt-awesome/ 2026-08-11T21:11:11Z https://github.com/tenstorrent/tt-system-firmware/releases/tag/v19.13.2 tt-system-firmware v19.13.2 2026-08-11T21:11:11Z KMD logging backend for streaming firmware logs over PCIe, and a stability fix for intermittent Tensix reset hangs by switching to NOC coordinates; check the migration guide when upgrading from v19.12.0.]]> Blackhole boards can now harvest individual GDDR channels per instance through a new firmware table field that's configurable at runtime, giving you finer control over memory defect handling without reflashing. Beyond that, v19.13.1–19.13.2 bring telemetry improvements across platforms (feature capability reporting on Blackhole, Metal runtime telemetry carve-outs on Wormhole), a new KMD logging backend for streaming firmware logs over PCIe, and a stability fix for intermittent Tensix reset hangs by switching to NOC coordinates; check the migration guide when upgrading from v19.12.0.

Links: Repository · v19.13.2

official · added 2026-08-11

]]> https://github.com/tenstorrent/tt-toplike/releases/tag/v0.8.0 tt-toplike v0.8.0 2026-08-11T20:30:47Z tt-toplike now exposes the full span of driver telemetry — including tt-kmd class attributes, live PCIe bandwidth, and tt-smi 6.x metrics — while fixing a bug where per-device data could shuffle between reads, making monitoring and profiling workflows far more reliable for developers instrumenting multi-device clusters.

Links: Repository · v0.8.0

official · added 2026-08-11

]]> https://github.com/tenstorrent/tt-smi/releases/tag/v6.2.0 tt-smi v6.2.0 2026-08-11T15:40:06Z tt-smi's telemetry table and snapshot output, giving you a unified view of your hardware's power consumption without hunting through per-chip metrics. This matters for capacity planning and understanding thermal headroom across your Tenstorrent clusters at a glance.]]> Total board power is now visible in tt-smi's telemetry table and snapshot output, giving you a unified view of your hardware's power consumption without hunting through per-chip metrics. This matters for capacity planning and understanding thermal headroom across your Tenstorrent clusters at a glance.

Links: Repository · v6.2.0

official · added 2026-08-11

]]> https://github.com/tenstorrent/tt-metal/releases/tag/v0.76.0 tt-metal v0.76.0 2026-08-11T04:10:19Z Quasar support now handles dual command queues, while a comprehensive sweep of data-race fixes in multicast and dataflow operations closes long-standing correctness gaps in distributed workloads—particularly in collective communication ops and dynamic tensor operations. On the kernel side, SFPU consolidation (#50399), improved numerical precision for transcendental functions, and broader Quasar performance test coverage reflect maturing silicon support. For developers building models, the Metal 2.0 migration continues accelerating (15+ factory ports completed) and per-core memory allocation now has a unified API (#51500), reducing complexity in sharded tensor workflows. A large batch of infrastructure improvements—from in-place wheel builds speeding up CI to agentic workflows automating triage—round out a release focused on correctness, performance, and developer velocity.]]> This release brings significant stability and feature improvements across low-level kernel execution, the core Metal runtime, and high-level neural network operations. Most notably, Quasar support now handles dual command queues, while a comprehensive sweep of data-race fixes in multicast and dataflow operations closes long-standing correctness gaps in distributed workloads—particularly in collective communication ops and dynamic tensor operations. On the kernel side, SFPU consolidation (#50399), improved numerical precision for transcendental functions, and broader Quasar performance test coverage reflect maturing silicon support. For developers building models, the Metal 2.0 migration continues accelerating (15+ factory ports completed) and per-core memory allocation now has a unified API (#51500), reducing complexity in sharded tensor workflows. A large batch of infrastructure improvements—from in-place wheel builds speeding up CI to agentic workflows automating triage—round out a release focused on correctness, performance, and developer velocity.

Links: Repository · v0.76.0

official · added 2026-08-11

]]> https://github.com/Zaneham/Booth/releases/tag/v0.5.2 BarraCUDA v0.5.2 2026-08-08T03:05:39Z sat there for a while because they only showed up once compilation finished. The structural story is the backend contract: targets now register themselves in a single list behind a be_desc_t, which shrunk main.c by a third and made adding a new target a matter of reading one header instead of 420 KB of AMD reference code. On the distribution side, Booth now installs properly with make install, ships static binaries for Linux, macOS, and Windows with no runtime dependencies, and exposes a CMake package so downstream projects can call booth_add_kernel() as part of their own build, with a worked example under examples/cmake/ and CI validation on every push.]]> Booth 0.5.2 fixes a series of silent failures where the compiler was cheerfully reporting success while the backend ran on rejected code, wrote output files over the same path, or lost precision without warning—problems that sat there for a while because they only showed up once compilation finished. The structural story is the backend contract: targets now register themselves in a single list behind a be_desc_t, which shrunk main.c by a third and made adding a new target a matter of reading one header instead of 420 KB of AMD reference code. On the distribution side, Booth now installs properly with make install, ships static binaries for Linux, macOS, and Windows with no runtime dependencies, and exposes a CMake package so downstream projects can call booth_add_kernel() as part of their own build, with a worked example under examples/cmake/ and CI validation on every push.

Links: Repository · v0.5.2

community · added 2026-08-08

]]> https://github.com/tenstorrent/ttsim/releases/tag/v1.10.0 ttsim v1.10.0 2026-08-07T21:33:47Z SFPSTORE, ZEROACC, and a handful of other core LLK operations now properly modeled across destination rows and clear modes—crucial if you're running real kernel code through ttsim and want the simulator to stay in sync with actual hardware behavior. You'll also find fresh support for BH QuietBox2 configurations, fixes to Ethernet state handling on WH, and better MMIO introspection, all aimed at closing the gap between simulation and metal.]]> QSR Tensix simulation is getting substantially more complete in this release, with SFPSTORE, ZEROACC, and a handful of other core LLK operations now properly modeled across destination rows and clear modes—crucial if you're running real kernel code through ttsim and want the simulator to stay in sync with actual hardware behavior. You'll also find fresh support for BH QuietBox2 configurations, fixes to Ethernet state handling on WH, and better MMIO introspection, all aimed at closing the gap between simulation and metal.

Links: Repository · v1.10.0

official · added 2026-08-07

]]> https://github.com/moritztng/tt-bio/releases/tag/v0.6.2 tt-bio v0.6.2 2026-08-07T21:33:46Z tt-bio predict --model openfold3, bringing the OpenFold Consortium's AlphaFold3 reproduction to Tenstorrent hardware—it folds proteins, RNA, and DNA chains with per-chain MSAs and optional templates, reusing the same scheduler, multi-card fan-out, and MSA cache as Protenix-v2. The model is gated against the official CPU reference across seven accuracy and parity legs, runs the on-device diffusion sampler in fp32 by default for parity, and strictly handles unsupported features (ligands, covalent bonds, PAE output) by raising rather than silently degrading; note that you'll need to fetch and point the OF3_CKPT environment variable at the consortium's public weights yourself. A handful of fixes shore up the debut—missing dependency declarations that broke clean installs, progress reporting wired through the trunk and sampler loops, and a tightened chunk gate (W<=384) that closed a 512-residue memory overflow—plus 1.18× GPU scaling improvements in the 300-residue range landed across Protenix-v2 and OpenDDE through kernel re-tuning (parity docs).]]> OpenFold3 is now live as tt-bio predict --model openfold3, bringing the OpenFold Consortium's AlphaFold3 reproduction to Tenstorrent hardware—it folds proteins, RNA, and DNA chains with per-chain MSAs and optional templates, reusing the same scheduler, multi-card fan-out, and MSA cache as Protenix-v2. The model is gated against the official CPU reference across seven accuracy and parity legs, runs the on-device diffusion sampler in fp32 by default for parity, and strictly handles unsupported features (ligands, covalent bonds, PAE output) by raising rather than silently degrading; note that you'll need to fetch and point the OF3_CKPT environment variable at the consortium's public weights yourself. A handful of fixes shore up the debut—missing dependency declarations that broke clean installs, progress reporting wired through the trunk and sampler loops, and a tightened chunk gate (W<=384) that closed a 512-residue memory overflow—plus 1.18× GPU scaling improvements in the 300-residue range landed across Protenix-v2 and OpenDDE through kernel re-tuning (parity docs).

Links: Repository · v0.6.2

affiliated · added 2026-08-07

]]> https://github.com/tenstorrent/luwen/releases/tag/bh-mod-v2.0.0 luwen bh-mod-v2.0.0 2026-08-07T14:05:56Z ccfgovr register and rejecting out-of-range values that the chip cannot accept—preventing silent misconfiguration that could lead to memory instability or undefined behavior downstream. If you're tuning Blackhole memory parameters, these validation checks ensure your settings actually match hardware constraints.]]> This release hardens DRAM harvest configuration on Blackhole by validating harvest field values before writing to the ccfgovr register and rejecting out-of-range values that the chip cannot accept—preventing silent misconfiguration that could lead to memory instability or undefined behavior downstream. If you're tuning Blackhole memory parameters, these validation checks ensure your settings actually match hardware constraints.

Links: Repository · bh-mod-v2.0.0

official · added 2026-08-07

]]> https://github.com/tenstorrent/sfpi/releases/tag/7.68.0 SFPI 7.68.0 2026-08-07T13:57:54Z -ffast-math flag to unlock—giving you more flexibility in fusing operations on-device. We've also fixed a subtle bug in ttreplay optimization that could leave the replay buffer partially empty, which should improve both performance and predictability for workloads relying on replay. A handful of stability and efficiency improvements round out the release, along with deprecation of vBool-to-vInt conversion, which had no real-world uses anyway.]]> The SFPI compiler now supports additional builtin combination operations—though some require the -ffast-math flag to unlock—giving you more flexibility in fusing operations on-device. We've also fixed a subtle bug in ttreplay optimization that could leave the replay buffer partially empty, which should improve both performance and predictability for workloads relying on replay. A handful of stability and efficiency improvements round out the release, along with deprecation of vBool-to-vInt conversion, which had no real-world uses anyway.

Links: Repository · 7.68.0

official · added 2026-08-07

]]> https://github.com/dstackai/dstack/releases/tag/0.21.0 dstack 0.21.0 2026-08-06T13:31:14Z dstack plugins, you'll need to verify they're compatible with Pydantic v2 models before upgrading. Beyond the foundational work, this release brings several UX wins: dstack metrics now shows CPU, memory, and GPU utilization as sparklines over the last hour so you can spot trends at a glance; AWS gateways with ACM certificates can now run multiple replicas with HTTPS handled directly by dstack instead of requiring an external load balancer; and the preset system gained baseline trials, per-trial learnings, and a richer CLI view to make benchmark results more interpretable. You'll also see GPU driver detection in dstack fleet -v and new --full-offers and --unallocated flags for better capacity discovery on Kubernetes and Slurm. Note that servers and CLIs must be upgraded together — new CLIs won't work with 0.20.x servers.]]> The jump to Pydantic v2 unblocks Python 3.14 support and opens the door to newer ecosystem libraries, while bringing faster validation and CLI startup — but if you're using the Python API or have dstack plugins, you'll need to verify they're compatible with Pydantic v2 models before upgrading. Beyond the foundational work, this release brings several UX wins: dstack metrics now shows CPU, memory, and GPU utilization as sparklines over the last hour so you can spot trends at a glance; AWS gateways with ACM certificates can now run multiple replicas with HTTPS handled directly by dstack instead of requiring an external load balancer; and the preset system gained baseline trials, per-trial learnings, and a richer CLI view to make benchmark results more interpretable. You'll also see GPU driver detection in dstack fleet -v and new --full-offers and --unallocated flags for better capacity discovery on Kubernetes and Slurm. Note that servers and CLIs must be upgraded together — new CLIs won't work with 0.20.x servers.

Links: Repository · 0.21.0

community · added 2026-08-06

]]> https://github.com/tenstorrent/ttnn-visualizer/releases/tag/v0.97.0 ttnn-visualizer v0.97.0 2026-08-05T17:46:17Z version checks no longer flag patch updates, SSH config is read to auto-populate remote connections, and performance charts got a tidier layout with grouped headings.]]> The visualizer now renders cluster topology and placement directly from the cluster descriptor without requiring a baked SoC descriptor, making it easier to inspect multi-chip systems on the fly—and when you do have per-chip arch data, it's applied as optional enrichment rather than a hard blocker. Multi-host profiling is cleaner too: remote sync can now discover and download performance reports per rank, and reads default to rank 0 to eliminate the visual collision of overlapping data from every rank. A few quality-of-life fixes round out the release—version checks no longer flag patch updates, SSH config is read to auto-populate remote connections, and performance charts got a tidier layout with grouped headings.

Links: Repository · v0.97.0

official · added 2026-08-05

]]> https://github.com/tsingletaryTT/tensix-viz/releases/tag/v1.2.0 tensix-viz v1.2.0 2026-08-05T16:35:19Z full changelog for implementation details.]]> tensix-viz v1.2.0 sharpens memory stats configuration for hardware-powered visuals, making it easier to instrument and monitor Tensix performance directly from visualization tools. This matters when you're profiling real workloads on silicon and need reliable memory telemetry baked into your dashboards without extra plumbing. Check the full changelog for implementation details.

Links: Repository · v1.2.0

affiliated · added 2026-08-05

]]> https://github.com/tenstorrent/tt-installer/releases/tag/v3.5.2 tt-installer v3.5.2 2026-07-30T18:00:15Z UV integration now respects existing virtual environments, so you won't hit friction if you're working in an already-active venv during installation—a small but meaningful quality-of-life improvement for developers with existing Python setups.

Links: Repository · v3.5.2

official · added 2026-07-30

]]> https://github.com/tenstorrent/tt-forge/releases/tag/1.4.0 tt-forge 1.4.0 2026-07-30T11:17:16Z supported model matrix, while internal tooling improvements (including a move of AI bringup scripts to a dedicated repo) clean up the monorepo and streamline testing workflows. The release includes updated PyTorch and dependency commits across tt-xla, tt-mlir, and tt-metal, ensuring compatibility with recent compiler and hardware stacks.]]> tt-forge 1.4.0 brings a comprehensive refresh of model coverage and infrastructure, now supporting everything from Llama 3.2 Vision to Qwen 3 and Falcon 3 across both n150 and p150 hardware. The most significant change is the expanded LLM lineup—you can now compile and deploy over 40 language model variants including the latest reasoning-focused OLMo3 models and multimodal systems like Pixtral—making it practical to benchmark performance across a much wider range of production targets. On the non-LLM side, embedding models, vision transformers, and diffusion backends round out the supported model matrix, while internal tooling improvements (including a move of AI bringup scripts to a dedicated repo) clean up the monorepo and streamline testing workflows. The release includes updated PyTorch and dependency commits across tt-xla, tt-mlir, and tt-metal, ensuring compatibility with recent compiler and hardware stacks.

Links: Repository · 1.4.0

official · added 2026-07-30

]]> https://github.com/tenstorrent/tt-forge-onnx/releases/tag/1.4.0 tt-forge-onnx 1.4.0 2026-07-30T11:12:02Z documentation migration from mdBook to Sphinx, which gives you a refreshed UI and improved navigation for guides and API references—worth checking out if you've been following along with the docs.]]> This release rolls up steady progress across tt-mlir and tt-forge-models dependencies, keeping the compiler stack current with upstream development. The highlight is a documentation migration from mdBook to Sphinx, which gives you a refreshed UI and improved navigation for guides and API references—worth checking out if you've been following along with the docs.

Links: Repository · 1.4.0

official · added 2026-07-30

]]> https://github.com/tenstorrent/tt-xla/releases/tag/1.4.0 tt-xla 1.4.0 2026-07-30T11:06:22Z Chunked prefill with decoupled buckets and runtime scheduling, plus multi-parallelism support across generative and pooling models, unlock more efficient vLLM inference on Tenstorrent hardware—letting you scale from single-device to data-parallel and tensor-parallel deployments without manual graph fragmentation. Beyond vLLM, you'll find new e2e generative model pipelines (Flux, Flux.2, Janus-Pro, Z-Image, GLM-Image), MLA attention in vLLM, mixed-precision quantization heuristics, improved SDPA handling for bool masks behind an opt-in flag, and fixes to device-count mismatches and layer-norm dtype issues under AOTAutograd, all tested against a growing roster of LLMs and image-generation models across n150, p150, and n300 hardware.

Links: Repository · 1.4.0

official · added 2026-07-30

]]> https://github.com/tenstorrent/tt-studio/releases/tag/rc-v2.9.0 TT-Studio rc-v2.9.0 2026-07-28T18:09:27Z .env friction for new users, and you can now edit the Voice Agent's system prompt at runtime to tune behavior on the fly. The workflow canvas got a substantial refresh—model auto-select, live code streaming, dynamic edge styling with smart deletion guards, and auto-picked RAG collections—alongside fresh model support (Flux Schnell) and parallel deployment to speed up initialization. A backend-unreachable overlay provides clearer feedback when connectivity drops, and the launcher now handles branch-switching and cleanup via --uninstall and --switch flags. The backend bumps to v0.18.0 of tt-inference-server and consolidates state management to keep the source tree clean.]]> Studio's first-run experience moves into the app itself with in-app setup and secrets management, eliminating the .env friction for new users, and you can now edit the Voice Agent's system prompt at runtime to tune behavior on the fly. The workflow canvas got a substantial refresh—model auto-select, live code streaming, dynamic edge styling with smart deletion guards, and auto-picked RAG collections—alongside fresh model support (Flux Schnell) and parallel deployment to speed up initialization. A backend-unreachable overlay provides clearer feedback when connectivity drops, and the launcher now handles branch-switching and cleanup via --uninstall and --switch flags. The backend bumps to v0.18.0 of tt-inference-server and consolidates state management to keep the source tree clean.

Links: Repository · rc-v2.9.0

official · added 2026-07-28

]]> https://github.com/tenstorrent/tt-inference-server/releases/tag/v0.19.0 tt-inference-server v0.19.0 2026-07-24T16:58:16Z Llama-3.1-8B-Instruct is now running on P300 hardware, expanding the inference server's device support for this widely-used instruction-tuned model. The uplift tracks the latest TT-Metal commits and maintains functional status, letting you deploy this capable 8B model on newer Tenstorrent accelerators.

Links: Repository · v0.19.0

official · added 2026-07-24

]]> https://github.com/tenstorrent/tt-topology/releases/tag/v1.2.20 tt-topology v1.2.20 2026-07-23T15:35:12Z pyluwen dependency to 0.8.5, bringing in improvements and fixes from that library that support tt-topology's hardware introspection and topology validation work. If you're using tt-topology to characterize or configure your Tenstorrent mesh, this update ensures you're working with the latest device communication layer.]]> This release bumps the underlying pyluwen dependency to 0.8.5, bringing in improvements and fixes from that library that support tt-topology's hardware introspection and topology validation work. If you're using tt-topology to characterize or configure your Tenstorrent mesh, this update ensures you're working with the latest device communication layer.

Links: Repository · v1.2.20

official · added 2026-07-23

]]> https://github.com/tenstorrent/tt-exalens/releases/tag/v0.3.29 tt-exalens v0.3.29 2026-07-22T10:37:50Z noc_read32 and noc_write32 were incorrectly routing through DMA instead of executing directly, which should resolve performance anomalies in code relying on those primitives.]]> Exalens now supports step functionality for Rocket cores, letting you pause and advance through Rocket code one instruction at a time—a critical addition for debugging complex kernel behavior. The release also improves memory introspection by exposing read and write memory bytes in the debug interface, and fixes a longstanding issue where noc_read32 and noc_write32 were incorrectly routing through DMA instead of executing directly, which should resolve performance anomalies in code relying on those primitives.

Links: Repository · v0.3.29

official · added 2026-07-22

]]> https://github.com/moritztng/tt-atom/releases/tag/v0.2.1 tt-atom v0.2.1 2026-07-20T11:06:13Z Calculator(atoms, model=...) cuts through three separate factory functions, dispatching automatically by checkpoint name just like FAIRChemCalculator or HF's AutoModel—a clean API win that makes Orb-v3 and UMA feel like one family to users. Beyond that surface change, OrbCalculator.evaluate_batch delivers measured 12–19× speedup on Orb inference (outpacing UMA's own ~13× batched gain), and the release consolidates the whole pipeline: fused_lnbw now defaults on when the hardware supports it, a machine-readable release gate replaces manual checklist steps, and the MultiCard worker—which was silently building only the UMA path—now correctly dispatches to Orb. Honest perf-per-dollar numbers land too: the p150 trails the H200 on raw throughput but wins by ~4–9× per dollar as systems scale, verified with committed raw timings; an earlier 40× claim that lacked evidence is withdrawn. Several measured fusion wins (closed-form geometry VJP, fused SiLU backward, row-major scatter) compound to real speedups, and a new MgO rock-salt multi-element bulk baseline rounds out the parity table.]]> The unified entry point Calculator(atoms, model=...) cuts through three separate factory functions, dispatching automatically by checkpoint name just like FAIRChemCalculator or HF's AutoModel—a clean API win that makes Orb-v3 and UMA feel like one family to users. Beyond that surface change, OrbCalculator.evaluate_batch delivers measured 12–19× speedup on Orb inference (outpacing UMA's own ~13× batched gain), and the release consolidates the whole pipeline: fused_lnbw now defaults on when the hardware supports it, a machine-readable release gate replaces manual checklist steps, and the MultiCard worker—which was silently building only the UMA path—now correctly dispatches to Orb. Honest perf-per-dollar numbers land too: the p150 trails the H200 on raw throughput but wins by ~4–9× per dollar as systems scale, verified with committed raw timings; an earlier 40× claim that lacked evidence is withdrawn. Several measured fusion wins (closed-form geometry VJP, fused SiLU backward, row-major scatter) compound to real speedups, and a new MgO rock-salt multi-element bulk baseline rounds out the parity table.

Links: Repository · v0.2.1

affiliated · added 2026-07-20

]]> https://github.com/tenstorrent/tt-vscode-toolkit/releases/tag/v0.1.19 tt-vscode-toolkit v0.1.19 2026-07-16T21:17:06Z The VS Code toolkit now includes a comprehensive guide to joyfully monkeypatching TT-NN without modifying your precious tt-metal checkouts.

Links: Repository · v0.1.19

official · added 2026-07-16

]]> https://github.com/tenstorrent/tt-burnin/releases/tag/v0.4.3 tt-burnin v0.4.3 2026-07-16T15:26:18Z #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.]]> 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.

Links: Repository · v0.4.3

official · added 2026-07-16

]]> https://github.com/tenstorrent/tt-kmd/releases/tag/ttkmd-2.10.0 tt-kmd ttkmd-2.10.0 2026-07-13T20:07:36Z 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 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.

Links: Repository · ttkmd-2.10.0

official · added 2026-07-13

]]> https://github.com/tenstorrent/tt-local-generator/releases/tag/v0.11.0 tt-local-generator v0.11.0 2026-07-10T16:47:57Z 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.

Links: Repository · v0.11.0

official · added 2026-07-10

]]> https://github.com/tenstorrent/tt-umd/releases/tag/v0.9.9 tt-umd v0.9.9 2026-07-09T17:03:46Z 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.

Links: Repository · v0.9.9

official · added 2026-07-09

]]> https://github.com/tenstorrent/tt-flash/releases/tag/v3.10.0 tt-flash v3.10.0 2026-06-23T19:59:38Z 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.

Links: Repository · v3.10.0

official · added 2026-06-23

]]> https://github.com/tenstorrent/tt-animatediff/releases/tag/v0.9.0 tt-animatediff v0.9.0 2026-06-22T19:56:31Z 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.

Links: Repository · v0.9.0

official · added 2026-06-22

]]> https://github.com/Zaneham/ttas/releases/tag/v0.1.0 ttas v0.1.0 2026-05-28T07:08:35Z 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.

Links: Repository · v0.1.0

community · added 2026-05-28

]]> https://github.com/tenstorrent/whisper/releases/tag/1.861 whisper 1.861 2026-05-11T15:44:36Z whisper released 1.861. Repository: https://github.com/tenstorrent/whisper

Links: Repository · 1.861

official · added 2026-05-11

]]> https://github.com/mesham/tt-sim/releases/tag/v1.0 tt-sim v1.0 2026-05-11T13:07:42Z 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.

Links: Repository · v1.0

community · added 2026-05-11

]]> https://github.com/tenstorrent-riscv-software/tt-bh-linux/releases/tag/v0.11 tt-bh-linux v0.11 2026-04-13T15:10:59Z 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.

Links: Repository · v0.11

official · added 2026-04-13

]]> https://github.com/tenstorrent/tt-firmware/releases/tag/v19.6.0 tt-firmware v19.6.0 2026-02-20T16:53:34Z 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.

Links: Repository · v19.6.0

official · added 2026-02-20

]]> https://github.com/Syllo/nvtop/releases/tag/3.3.2 nvtop 3.3.2 2026-02-08T17:57:16Z 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.

Links: Repository · 3.3.2

community · added 2026-02-08

]]> https://github.com/tenstorrent/tt-tools-common/releases/tag/v1.6.0 tt-tools-common v1.6.0 2025-12-23T21:02:08Z python3-tt-tools-common in the Tenstorrent PPA, so the shared plumbing under tt-smi, tt-flash, and friends installs cleanly via apt.]]> 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.

Links: Repository · v1.6.0

official · added 2025-12-23

]]> https://github.com/tenstorrent/tt-system-tools/releases/tag/v1.4.1 tt-system-tools v1.4.1 2025-12-08T17:23:48Z tt-system-tools released v1.4.1. Repository: https://github.com/tenstorrent/tt-system-tools

Links: Repository · v1.4.1

official · added 2025-12-08

]]> https://github.com/tenstorrent/riescue/releases/tag/v1.7.0 RiESCUE v1.7.0 2025-12-03T19:29:44Z 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.]]> 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.

Links: Repository · v1.7.0

official · added 2025-12-03

]]> https://github.com/tenstorrent/tt-torch/releases/tag/0.4.0 tt-torch 0.4.0 2025-09-29T22:23:47Z tt-torch released 0.4.0. Repository: https://github.com/tenstorrent/tt-torch

Links: Repository · 0.4.0

official · added 2025-09-29

]]> https://github.com/tenstorrent/polaris/releases/tag/pre_perfmodel_merge polaris pre_perfmodel_merge 2025-09-19T18:16:27Z polaris released pre_perfmodel_merge. Repository: https://github.com/tenstorrent/polaris

Links: Repository · pre_perfmodel_merge

official · added 2025-09-19

]]> https://github.com/tenstorrent/riscv_arch_tests/releases/tag/v0.2.0%2Baligned-access riscv_arch_tests v0.2.0+aligned-access 2025-01-23T17:16:16Z 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.

Links: Repository · v0.2.0+aligned-access

official · added 2025-01-23

]]> https://github.com/tenstorrent/tt-buda/releases/tag/v0.19.3 tt-buda v0.19.3 2024-09-24T21:01:08Z 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.

Links: Repository · v0.19.3

official · added 2024-09-24

]]> https://github.com/zk4x/zyx/releases/tag/v0.14.0 zyx v0.14.0 2024-09-22T13:54:32Z zyx released v0.14.0. Repository: https://github.com/zk4x/zyx

Links: Repository · v0.14.0

community · added 2024-09-22

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