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<feed xmlns="http://www.w3.org/2005/Atom">
  <title>tt-awesome — New Entries</title>
  <subtitle>Newly added projects, tools, and resources in the Tenstorrent ecosystem</subtitle>
  <link href="https://tenstorrent.github.io/tt-awesome/feeds/new-entries.xml" rel="self"/>
  <link href="https://tenstorrent.github.io/tt-awesome/"/>
  <id>https://tenstorrent.github.io/tt-awesome/feeds/new-entries.xml</id>
  <author><name>Tenstorrent Community</name><uri>https://tenstorrent.github.io/tt-awesome/</uri></author>
  <updated>2026-07-17T23:59:59Z</updated>
  <entry>
    <id>https://github.com/tenstorrent/sfpi</id>
    <title>SFPI</title>
    <link href="https://github.com/tenstorrent/sfpi"/>
    <updated>2026-07-17T23:59:59Z</updated>
    <summary type="html"><![CDATA[Tenstorrent SFPU programming interface — TT-enhanced RISC-V GCC and binutils plus header files for programming the Tensix SFPU (vector engine) from kernel code. The compiler toolchain underneath TT-Metalium's SFPU ops.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent SFPU programming interface — TT-enhanced RISC-V GCC and binutils plus header files for programming the Tensix SFPU (vector engine) from kernel code. The compiler toolchain underneath TT-Metalium's SFPU ops.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/sfpi">Repo</a></p>
<p>official · added 2026-07-17</p>
<p><em>sfpu, compiler-toolchain, gcc, riscv, kernels, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="kernels"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-tools-common</id>
    <title>tt-tools-common</title>
    <link href="https://github.com/tenstorrent/tt-tools-common"/>
    <updated>2026-07-17T23:59:58Z</updated>
    <summary type="html"><![CDATA[Shared helper library of common utilities used across Tenstorrent system tools such as tt-smi, tt-flash, and tt-topology. A dependency rather than a standalone tool.]]></summary>
    <content type="html"><![CDATA[<p>Shared helper library of common utilities used across Tenstorrent system tools such as tt-smi, tt-flash, and tt-topology. A dependency rather than a standalone tool.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-tools-common">Repo</a></p>
<p>official · added 2026-07-17</p>
<p><em>library, tooling, shared-utilities, hw-system</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-system-tools</id>
    <title>tt-system-tools</title>
    <link href="https://github.com/tenstorrent/tt-system-tools"/>
    <updated>2026-07-17T23:59:57Z</updated>
    <summary type="html"><![CDATA[System setup and support utilities for Tenstorrent hardware — hugepages-setup configures the 1GB hugepages TT ASICs need, and tt-oops collects diagnostic data for troubleshooting. Ships as the tenstorrent-tools deb/rpm.]]></summary>
    <content type="html"><![CDATA[<p>System setup and support utilities for Tenstorrent hardware — hugepages-setup configures the 1GB hugepages TT ASICs need, and tt-oops collects diagnostic data for troubleshooting. Ships as the tenstorrent-tools deb/rpm.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-system-tools">Repo</a></p>
<p>official · added 2026-07-17</p>
<p><em>hugepages, system-setup, diagnostics, hw-system</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-burnin</id>
    <title>tt-burnin</title>
    <link href="https://github.com/tenstorrent/tt-burnin"/>
    <updated>2026-07-17T23:59:56Z</updated>
    <summary type="html"><![CDATA[Command-line utility that runs a high power-consumption workload on Tenstorrent devices — used for chip testing, burn-in, and validating a system's power delivery and cooling under sustained load.]]></summary>
    <content type="html"><![CDATA[<p>Command-line utility that runs a high power-consumption workload on Tenstorrent devices — used for chip testing, burn-in, and validating a system's power delivery and cooling under sustained load.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-burnin">Repo</a></p>
<p>official · added 2026-07-17</p>
<p><em>burn-in, stress-test, power, hardware-validation, hw-system</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/riscv_arch_tests</id>
    <title>riscv_arch_tests</title>
    <link href="https://github.com/tenstorrent/riscv_arch_tests"/>
    <updated>2026-07-10T23:59:55Z</updated>
    <summary type="html"><![CDATA[RISC-V architectural self-checking directed tests — randomly-generated register operands and data with low-level OS code for test scheduling and self-checking, runnable on a RISC-V design or an ISS such as Whisper or Spike. Generated by an internal Tenstorrent tool from the official RISC-V ISA spec.]]></summary>
    <content type="html"><![CDATA[<p>RISC-V architectural self-checking directed tests — randomly-generated register operands and data with low-level OS code for test scheduling and self-checking, runnable on a RISC-V design or an ISS such as Whisper or Spike. Generated by an internal Tenstorrent tool from the official RISC-V ISA spec.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/riscv_arch_tests">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>riscv, testing, verification, isa, architecture, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-isa-documentation</id>
    <title>tt-isa-documentation</title>
    <link href="https://github.com/tenstorrent/tt-isa-documentation"/>
    <updated>2026-07-10T23:59:54Z</updated>
    <summary type="html"><![CDATA[Low-level ISA and microarchitecture documentation for Tenstorrent AI architectures (Grayskull, Wormhole, Blackhole) — the authoritative hardware reference beneath the tt-forge / tt-metal software stack.]]></summary>
    <content type="html"><![CDATA[<p>Low-level ISA and microarchitecture documentation for Tenstorrent AI architectures (Grayskull, Wormhole, Blackhole) — the authoritative hardware reference beneath the tt-forge / tt-metal software stack.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-isa-documentation">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>isa, architecture, documentation, tensix, low-level, research, guides</em></p>]]></content>
    <category term="official"/>
    <category term="research"/>
    <category term="guides"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/polaris</id>
    <title>polaris</title>
    <link href="https://github.com/tenstorrent/polaris"/>
    <updated>2026-07-10T23:59:53Z</updated>
    <summary type="html"><![CDATA[A high-level AI simulator from Tenstorrent for modeling and exploring AI accelerator and workload performance.]]></summary>
    <content type="html"><![CDATA[<p>A high-level AI simulator from Tenstorrent for modeling and exploring AI accelerator and workload performance.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/polaris">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>simulator, performance, modeling, architecture, research, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="research"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-forge-models</id>
    <title>tt-forge-models</title>
    <link href="https://github.com/tenstorrent/tt-forge-models"/>
    <updated>2026-07-10T23:59:52Z</updated>
    <summary type="html"><![CDATA[A shared repository of model implementations used across TT-Forge frontends — a single source of truth for the models used in testing and benchmarking, rather than duplicating them across frontend repos.]]></summary>
    <content type="html"><![CDATA[<p>A shared repository of model implementations used across TT-Forge frontends — a single source of truth for the models used in testing and benchmarking, rather than duplicating them across frontend repos.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-forge-models">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>tt-forge, models, benchmarking, testing, inference, ai-models</em></p>]]></content>
    <category term="official"/>
    <category term="ai-models"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-perf-report</id>
    <title>tt-perf-report</title>
    <link href="https://github.com/tenstorrent/tt-perf-report"/>
    <updated>2026-07-10T23:59:51Z</updated>
    <summary type="html"><![CDATA[Performance report analysis tool for Tenstorrent Metal operations — analyzes perf traces to surface throughput, bottlenecks, and optimization opportunities.]]></summary>
    <content type="html"><![CDATA[<p>Performance report analysis tool for Tenstorrent Metal operations — analyzes perf traces to surface throughput, bottlenecks, and optimization opportunities.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-perf-report">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>performance, profiling, tt-metal, analysis, optimization, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-emule</id>
    <title>tt-emule</title>
    <link href="https://github.com/tenstorrent/tt-emule"/>
    <updated>2026-07-10T23:59:50Z</updated>
    <summary type="html"><![CDATA[A C++ software emulator of the Tenstorrent device-level kernel and host APIs. Run tt-metal kernel and host code on a standard x86-64 Linux machine — no Tenstorrent hardware required.]]></summary>
    <content type="html"><![CDATA[<p>A C++ software emulator of the Tenstorrent device-level kernel and host APIs. Run tt-metal kernel and host code on a standard x86-64 Linux machine — no Tenstorrent hardware required.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-emule">Repo</a></p>
<p>official · added 2026-07-10</p>
<p><em>emulator, tt-metal, no-hardware, testing, kernels, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/moritztng/tt-atom</id>
    <title>tt-atom</title>
    <link href="https://github.com/moritztng/tt-atom"/>
    <updated>2026-07-10T23:59:49Z</updated>
    <summary type="html"><![CDATA[Meta's UMA interatomic potential running on Tenstorrent Blackhole — energy, forces, and stress for molecules and periodic materials behind an ASE calculator. Its per-edge Wigner rotation runs as a custom tt-metal kernel for a highest-performance uma-s build.]]></summary>
    <content type="html"><![CDATA[<p>Meta's UMA interatomic potential running on Tenstorrent Blackhole — energy, forces, and stress for molecules and periodic materials behind an ASE calculator. Its per-edge Wigner rotation runs as a custom tt-metal kernel for a highest-performance uma-s build.</p>

<p><strong>Links:</strong> <a href="https://github.com/moritztng/tt-atom">Repo</a></p>
<p>By <a href="https://github.com/moritztng">@moritztng</a> · affiliated · added 2026-07-10</p>
<p><em>molecular-dynamics, interatomic-potential, mlip, uma, ase, inference, custom-kernel, ai-models</em></p>]]></content>
    <category term="affiliated"/>
    <category term="ai-models"/>
  </entry>
  <entry>
    <id>https://github.com/pcmoritz/libtt</id>
    <title>libtt</title>
    <link href="https://github.com/pcmoritz/libtt"/>
    <updated>2026-07-10T23:59:48Z</updated>
    <summary type="html"><![CDATA[A Bazel-built PJRT plugin (libtt.so) providing an XLA backend for Tenstorrent devices. Bundles the tt-xla PJRT implementation with tt-mlir and tt-metal into a single shared object so JAX code runs on Tenstorrent hardware, with patches so sglang-jax works out of the box.]]></summary>
    <content type="html"><![CDATA[<p>A Bazel-built PJRT plugin (libtt.so) providing an XLA backend for Tenstorrent devices. Bundles the tt-xla PJRT implementation with tt-mlir and tt-metal into a single shared object so JAX code runs on Tenstorrent hardware, with patches so sglang-jax works out of the box.</p>

<p><strong>Links:</strong> <a href="https://github.com/pcmoritz/libtt">Repo</a></p>
<p>By <a href="https://github.com/pcmoritz">@Philipp Moritz</a> · community · added 2026-07-10</p>
<p><em>xla, pjrt, jax, bazel, sglang, compilers</em></p>]]></content>
    <category term="community"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://github.com/marty1885/ttPseudoRowMajor</id>
    <title>ttPseudoRowMajor</title>
    <link href="https://github.com/marty1885/ttPseudoRowMajor"/>
    <updated>2026-07-10T23:59:47Z</updated>
    <summary type="html"><![CDATA[A small TTNN-facing C++ library (ttprm) for running view-shaped tensor work without first materializing the view in DRAM. Targets Tenstorrent TILE tensors and uses cached device operations to gather/scatter through layout views.]]></summary>
    <content type="html"><![CDATA[<p>A small TTNN-facing C++ library (ttprm) for running view-shaped tensor work without first materializing the view in DRAM. Targets Tenstorrent TILE tensors and uses cached device operations to gather/scatter through layout views.</p>

<p><strong>Links:</strong> <a href="https://github.com/marty1885/ttPseudoRowMajor">Repo</a></p>
<p>By <a href="https://github.com/marty1885">@Martin Chang</a> · community · added 2026-07-10</p>
<p><em>ttnn, tensor, tile, layout, cpp, kernels</em></p>]]></content>
    <category term="community"/>
    <category term="kernels"/>
  </entry>
  <entry>
    <id>https://github.com/zk4x/zyx</id>
    <title>zyx</title>
    <link href="https://github.com/zk4x/zyx"/>
    <updated>2026-07-03T23:59:46Z</updated>
    <summary type="html"><![CDATA[A complete ML library and compiler in Rust — &quot;from assembly to neural networks&quot; — with a native Tenstorrent backend (src/backend/tenstorrent), autograd, custom kernels, multi-backend support, and Python bindings.]]></summary>
    <content type="html"><![CDATA[<p>A complete ML library and compiler in Rust — &quot;from assembly to neural networks&quot; — with a native Tenstorrent backend (src/backend/tenstorrent), autograd, custom kernels, multi-backend support, and Python bindings.</p>

<p><strong>Links:</strong> <a href="https://github.com/zk4x/zyx">Repo</a> · <a href="https://docs.rs/zyx">Website</a></p>
<p>By <a href="https://github.com/zk4x">@zk4x</a> · community · added 2026-07-03</p>
<p><em>rust, ml-compiler, tensor-library, autograd, backend, compilers, dev-tools</em></p>]]></content>
    <category term="community"/>
    <category term="compilers"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/RQM-Technologies-dev/tt-rqm-kernels</id>
    <title>tt-rqm-kernels</title>
    <link href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels"/>
    <updated>2026-07-03T23:59:45Z</updated>
    <summary type="html"><![CDATA[Structured quaternion, rotor, and phase-aware tensor kernels — operations on 3D rotation and orientation data packed into ordinary <code>[N, 4]</code> float tensors — plus StructuredBench, a benchmark suite for these workloads. Provides CPU/PyTorch reference implementations and an optional TT-Lang simulator prototype for the quaternion multiply (<code>qmul</code>) kernel as a first step toward Tenstorrent hardware support.]]></summary>
    <content type="html"><![CDATA[<p>Structured quaternion, rotor, and phase-aware tensor kernels — operations on 3D rotation and orientation data packed into ordinary <code>[N, 4]</code> float tensors — plus StructuredBench, a benchmark suite for these workloads. Provides CPU/PyTorch reference implementations and an optional TT-Lang simulator prototype for the quaternion multiply (<code>qmul</code>) kernel as a first step toward Tenstorrent hardware support.</p>

<p><strong>Links:</strong> <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels">Repo</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/docs/tenstorrent-landing.md">Tenstorrent landing page</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/docs/structuredbench-spec.md">StructuredBench specification</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/reports/tt_emule_qmul_candidate.md">tt-emule qmul candidate report</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/docs/tt-lang-qmul-plan.md">TT-Lang qmul plan</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/reports/tt_lang_qmul_sim.md">TT-Lang simulator report</a> · <a href="https://github.com/RQM-Technologies-dev/tt-rqm-kernels/blob/main/docs/tenstorrent-rfc.md">Tenstorrent RFC</a></p>
<p>By <a href="https://github.com/RQM-Technologies-dev">@RQM-Technologies-dev</a> · community · added 2026-07-03</p>
<p><em>structured-tensors, quaternion, rotor, tt-lang, simulator, benchmarks, pytorch, custom-kernels, kernels, research</em></p>]]></content>
    <category term="community"/>
    <category term="kernels"/>
    <category term="research"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#cloud-native-support</id>
    <title>Cloud-Native Support</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#cloud-native-support"/>
    <updated>2026-07-01T23:59:44Z</updated>
    <summary type="html"><![CDATA[Official documentation hub for running Tenstorrent accelerators on Kubernetes. Centers on tt-operator (the umbrella Helm chart) and covers Node Feature Discovery, kernel-mode driver (tt-kmd) management, firmware flashing, Prometheus telemetry, Fabric Manager topology resolution, Dynamic Resource Allocation, and multi-node scheduling via JobSet and PMIx.]]></summary>
    <content type="html"><![CDATA[<p>Official documentation hub for running Tenstorrent accelerators on Kubernetes. Centers on tt-operator (the umbrella Helm chart) and covers Node Feature Discovery, kernel-mode driver (tt-kmd) management, firmware flashing, Prometheus telemetry, Fabric Manager topology resolution, Dynamic Resource Allocation, and multi-node scheduling via JobSet and PMIx.</p>

<p><strong>Links:</strong> <a href="https://docs.tenstorrent.com/cloud-native-support/">docs.tenstorrent.com</a></p>
<p>official · added 2026-07-01</p>
<p><em>kubernetes, cloud-native, helm, tt-operator, orchestration, documentation, cloud-infra, guides</em></p>]]></content>
    <category term="official"/>
    <category term="cloud-infra"/>
    <category term="guides"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-operator</id>
    <title>tt-operator</title>
    <link href="https://github.com/tenstorrent/tt-operator"/>
    <updated>2026-07-01T23:59:43Z</updated>
    <summary type="html"><![CDATA[Kubernetes operator that automates installation and lifecycle management of the full software stack needed to run Tenstorrent workloads on a cluster. Distributed as an umbrella Helm chart coordinating driver (tt-kmd) management, Node Feature Discovery, firmware flashing, Prometheus telemetry, fabric/topology resolution, and device allocation with multi-node scheduling (JobSet/PMIx).]]></summary>
    <content type="html"><![CDATA[<p>Kubernetes operator that automates installation and lifecycle management of the full software stack needed to run Tenstorrent workloads on a cluster. Distributed as an umbrella Helm chart coordinating driver (tt-kmd) management, Node Feature Discovery, firmware flashing, Prometheus telemetry, fabric/topology resolution, and device allocation with multi-node scheduling (JobSet/PMIx).</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-operator">Repo</a> · <a href="https://docs.tenstorrent.com/cloud-native-support/">Cloud-Native Support docs</a></p>
<p>official · added 2026-07-01</p>
<p><em>kubernetes, helm, operator, orchestration, cloud-native, tt-kmd, device-plugin, cloud-infra, hw-system</em></p>]]></content>
    <category term="official"/>
    <category term="cloud-infra"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-quietbox2-guide</id>
    <title>TT-QuietBox 2 Guide</title>
    <link href="https://github.com/tenstorrent/tt-quietbox2-guide"/>
    <updated>2026-06-30T23:59:42Z</updated>
    <summary type="html"><![CDATA[Official setup and onboarding guide for the TT-QuietBox 2 — a compact, liquid-cooled AI workstation with four Blackhole accelerators, an AMD Ryzen CPU, 256GB RAM, and 4TB NVMe. Covers hardware specs, first-boot setup, and hands-on learning paths for running pre-loaded models like Qwen3-32B and serving text, image, video, and speech models via tt-inference-server.]]></summary>
    <content type="html"><![CDATA[<p>Official setup and onboarding guide for the TT-QuietBox 2 — a compact, liquid-cooled AI workstation with four Blackhole accelerators, an AMD Ryzen CPU, 256GB RAM, and 4TB NVMe. Covers hardware specs, first-boot setup, and hands-on learning paths for running pre-loaded models like Qwen3-32B and serving text, image, video, and speech models via tt-inference-server.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-quietbox2-guide">Repo</a> · <a href="https://docs.tenstorrent.com/tt-quietbox2-guide">TT-QuietBox 2 Guide</a></p>
<p>official · added 2026-06-30</p>
<p><em>quietbox, blackhole, workstation, setup, getting-started, documentation, hw-system, guides, getting-started</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
    <category term="guides"/>
    <category term="getting-started"/>
  </entry>
  <entry>
    <id>https://github.com/tetsuh/tt-metal-community-distro-matrix</id>
    <title>tetsuh/tt-metal-community-distro-matrix</title>
    <link href="https://github.com/tetsuh/tt-metal-community-distro-matrix"/>
    <updated>2026-06-29T23:59:41Z</updated>
    <summary type="html"><![CDATA[A compatibility guardrail that continuously monitors whether <a href="https://github.com/tenstorrent/tt-metal">tt-metal</a> and the official <a href="https://github.com/tenstorrent/tt-installer">tt-installer</a> build successfully on community Linux distributions that are not part of Tenstorrent's official CI.]]></summary>
    <content type="html"><![CDATA[<p>A compatibility guardrail that continuously monitors whether <a href="https://github.com/tenstorrent/tt-metal">tt-metal</a> and the official <a href="https://github.com/tenstorrent/tt-installer">tt-installer</a> build successfully on community Linux distributions that are not part of Tenstorrent's official CI.</p>

<p><strong>Links:</strong> <a href="https://github.com/tetsuh/tt-metal-community-distro-matrix">Repo</a></p>
<p>By <a href="https://github.com/tetsuh">@tetsuh</a> · community · added 2026-06-29</p>
<p><em>hw-system</em></p>]]></content>
    <category term="community"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/kinginu/tt-splat</id>
    <title>tt-splat — matrix-native 3D Gaussian Splatting on Blackhole</title>
    <link href="https://github.com/kinginu/tt-splat"/>
    <updated>2026-06-29T23:59:40Z</updated>
    <summary type="html"><![CDATA[3D Gaussian Splatting rewritten to run on the matrix engine: a polynomial splat and order-independent weighted-sum blending replace exp and depth-sorted alpha, so the pipeline becomes GEMM → activation → GEMM. Renderer + trainer, trained device-resident on a Blackhole p150a.]]></summary>
    <content type="html"><![CDATA[<p>3D Gaussian Splatting rewritten to run on the matrix engine: a polynomial splat and order-independent weighted-sum blending replace exp and depth-sorted alpha, so the pipeline becomes GEMM → activation → GEMM. Renderer + trainer, trained device-resident on a Blackhole p150a.</p>

<p><strong>Links:</strong> <a href="https://github.com/kinginu/tt-splat">Repo</a></p>
<p>By <a href="https://github.com/kinginu">@kinginu</a> · community · added 2026-06-29</p>
<p><em>3d-gaussian-splatting, 3dgs, rendering, matrix-engine, weighted-sum-rendering, kernels, research</em></p>]]></content>
    <category term="community"/>
    <category term="kernels"/>
    <category term="research"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/ttsim-qemu</id>
    <title>ttsim-qemu</title>
    <link href="https://github.com/tenstorrent/ttsim-qemu"/>
    <updated>2026-06-16T23:59:39Z</updated>
    <summary type="html"><![CDATA[Tenstorrent's fork of QEMU that provides the full-system emulation layer behind ttsim. Models the RISC-V cores and system devices of Wormhole and Blackhole so TT-Metalium workloads can boot and run without physical silicon.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent's fork of QEMU that provides the full-system emulation layer behind ttsim. Models the RISC-V cores and system devices of Wormhole and Blackhole so TT-Metalium workloads can boot and run without physical silicon.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/ttsim-qemu">Repo</a></p>
<p>official · added 2026-06-16</p>
<p><em>simulator, qemu, full-system, emulation, no-hardware, wormhole, blackhole, riscv-arch, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-forge-onnx</id>
    <title>tt-forge-onnx</title>
    <link href="https://github.com/tenstorrent/tt-forge-onnx"/>
    <updated>2026-06-15T23:59:38Z</updated>
    <summary type="html"><![CDATA[ONNX graph compiler for Tenstorrent hardware. Optimizes and transforms ONNX model graphs for efficient execution on Tensix accelerators. Used as a backend by tt-forge for ONNX model ingestion.]]></summary>
    <content type="html"><![CDATA[<p>ONNX graph compiler for Tenstorrent hardware. Optimizes and transforms ONNX model graphs for efficient execution on Tensix accelerators. Used as a backend by tt-forge for ONNX model ingestion.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-forge-onnx">Repo</a></p>
<p>official · added 2026-06-15</p>
<p><em>onnx, compiler, graph-optimization, mlir, compilers</em></p>]]></content>
    <category term="official"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-animatediff</id>
    <title>tt-animatediff</title>
    <link href="https://github.com/tenstorrent/tt-animatediff"/>
    <updated>2026-06-03T23:59:37Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-animatediff">Repo</a> · <a href="https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/animatediff-video-generation/">Native Video Animation with AnimateDiff (VSCode Toolkit)</a></p>
<p>official · added 2026-06-03</p>
<p><em>animatediff, video-generation, stable-diffusion, diffusion, gif, blackhole, ai-models, games-demos</em></p>]]></content>
    <category term="official"/>
    <category term="ai-models"/>
    <category term="games-demos"/>
  </entry>
  <entry>
    <id>https://github.com/Zaneham/ttas</id>
    <title>ttas</title>
    <link href="https://github.com/Zaneham/ttas"/>
    <updated>2026-05-27T23:59:36Z</updated>
    <summary type="html"><![CDATA[ttas is a hacker-friendly assembler/disassembler for Tensix on Wormhole. It turns assembly into the exact 32-bit words the hardware runs, and turns binaries back into readable instructions using the same shared instruction table.]]></summary>
    <content type="html"><![CDATA[<p>ttas is a hacker-friendly assembler/disassembler for Tensix on Wormhole. It turns assembly into the exact 32-bit words the hardware runs, and turns binaries back into readable instructions using the same shared instruction table.</p>

<p><strong>Links:</strong> <a href="https://github.com/Zaneham/ttas">Repo</a></p>
<p>By <a href="https://github.com/Zaneham">@Zaneham</a> · community · added 2026-05-27</p>
<p><em>assembler, dev-tools, hw-system</em></p>]]></content>
    <category term="community"/>
    <category term="dev-tools"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/Knight-Ops/libtt-metal-cxx</id>
    <title>libtt-metal-cxx</title>
    <link href="https://github.com/Knight-Ops/libtt-metal-cxx"/>
    <updated>2026-05-20T23:59:35Z</updated>
    <summary type="html"><![CDATA[Rust crate that exposes the TT-Metal host API through a C++ bridge via cxx.rs — covering device management, program/kernel creation (from source file or inline string), circular buffers, semaphores, runtime arguments, sharded buffers, and MeshDevice workflows, with hardware-backed integration tests.]]></summary>
    <content type="html"><![CDATA[<p>Rust crate that exposes the TT-Metal host API through a C++ bridge via cxx.rs — covering device management, program/kernel creation (from source file or inline string), circular buffers, semaphores, runtime arguments, sharded buffers, and MeshDevice workflows, with hardware-backed integration tests.</p>

<p><strong>Links:</strong> <a href="https://github.com/Knight-Ops/libtt-metal-cxx">Repo</a></p>
<p>By <a href="https://github.com/Knight-Ops">@Knight-Ops</a> · community · added 2026-05-20</p>
<p><em>rust, bindings, cxx, tt-metal, ffi, host-api, dev-tools</em></p>]]></content>
    <category term="community"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-llk</id>
    <title>tt-llk</title>
    <link href="https://github.com/tenstorrent/tt-llk"/>
    <updated>2026-05-13T23:59:34Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-llk">Repo</a> · <a href="https://github.com/tenstorrent/tt-llk/blob/main/docs/llk/l2/top_level_overview.md">Top-level architecture overview</a></p>
<p>official · added 2026-05-13</p>
<p><em>tensix, risc-v, llk, trisc, brisc, ncrisc, low-level, compute-engine, kernels, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="kernels"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#lecture-wm-csci654-tenstorrent</id>
    <title>Tenstorrent Architecture — W&amp;M CSCI654 Advanced Computer Architecture</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#lecture-wm-csci654-tenstorrent"/>
    <updated>2026-05-13T23:59:33Z</updated>
    <summary type="html"><![CDATA[Lecture 20 from William &amp; 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.]]></summary>
    <content type="html"><![CDATA[<p>Lecture 20 from William &amp; 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.</p>

<p><strong>Links:</strong> <a href="https://www.youtube.com/watch?v=CixEFPc8oxg">Lecture 20 — Tenstorrent Architecture (YouTube)</a></p>
<p>By Yifan &amp; GPU / William &amp; Mary · community · added 2026-05-13</p>
<p><em>lecture, architecture, wormhole, tensix, risc-v, sfpu, noc, academia, guides, riscv-arch</em></p>]]></content>
    <category term="community"/>
    <category term="guides"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-attention-grayskull</id>
    <title>Attention in SRAM on Tenstorrent Grayskull</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-attention-grayskull"/>
    <updated>2026-05-13T23:59:32Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2407.13885">arXiv:2407.13885</a></p>
<p>By Moritz Thüning · community · added 2026-05-13</p>
<p><em>attention, transformer, sram, grayskull, kernel, risc-v, research, kernels</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
    <category term="kernels"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-matmul-grayskull</id>
    <title>Assessing Tenstorrent Grayskull RISC-V MatMul Acceleration for LLMs</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-matmul-grayskull"/>
    <updated>2026-05-13T23:59:31Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2505.06085">arXiv:2505.06085</a></p>
<p>By Hiari Pizzini Cavagna, Daniele Cesarini, Andrea Bartolini · community · added 2026-05-13</p>
<p><em>matmul, grayskull, risc-v, bfp8, lofi, llm, precision, research</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-nbody-strategies-wormhole</id>
    <title>Porting Strategies for Gravitational N-Body Simulations on Tenstorrent Wormhole</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-nbody-strategies-wormhole"/>
    <updated>2026-05-13T23:59:30Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2605.02744">arXiv:2605.02744</a></p>
<p>By Jenny Lynn Almerol, Elisabetta Boella, Mario Spera, Daniele Gregori · community · added 2026-05-13</p>
<p><em>n-body, astrophysics, hpc, wormhole, risc-v, multi-accelerator, simulation, research</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-tileloom</id>
    <title>TileLoom: Automatic Dataflow Planning for Spatial Dataflow Accelerators</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-tileloom"/>
    <updated>2026-05-13T23:59:29Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2512.22168">arXiv:2512.22168</a></p>
<p>By Wei Li, Zhenyu Bai, Heru Wang, Pranav Dangi · community · added 2026-05-13</p>
<p><em>compiler, dataflow, spatial-accelerator, tile-based, on-chip-network, wormhole, research, compilers</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-tts-lightning</id>
    <title>Rewriting TTS Inference Economics: Lightning V2 on Tenstorrent vs. NVIDIA L40S</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-tts-lightning"/>
    <updated>2026-05-13T23:59:28Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2604.03279">arXiv:2604.03279</a></p>
<p>By Ranjith M. S., Akshat Mandloi, Sudarshan Kamath · community · added 2026-05-13</p>
<p><em>tts, text-to-speech, inference, bfp8, lofi, cost-efficiency, precision, research, ai-models</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
    <category term="ai-models"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#blog-anuraagw-blackhole-arch</id>
    <title>Tenstorrent Blackhole Architecture Guide</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#blog-anuraagw-blackhole-arch"/>
    <updated>2026-05-13T23:59:27Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://anuraagw.me/blog/blackhole-architecture">anuraagw.me — February 2026</a></p>
<p>By boopdotpng · community · added 2026-05-13</p>
<p><em>blackhole, architecture, tensix, noc, sifive-x280, firmware, mop, sfpu, deep-dive, blog, riscv-arch, guides</em></p>]]></content>
    <category term="community"/>
    <category term="riscv-arch"/>
    <category term="guides"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#paper-stencils-wormhole</id>
    <title>Stencil Computations on Tenstorrent Wormhole</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#paper-stencils-wormhole"/>
    <updated>2026-05-12T23:59:26Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://arxiv.org/abs/2605.07599">arXiv:2605.07599</a></p>
<p>community · added 2026-05-12</p>
<p><em>stencil, hpc, wormhole, risc-v, energy-efficiency, benchmarks, dataflow, research</em></p>]]></content>
    <category term="community"/>
    <category term="research"/>
  </entry>
  <entry>
    <id>https://tenstorrent.github.io/tt-awesome/#tt-console</id>
    <title>TT Console</title>
    <link href="https://tenstorrent.github.io/tt-awesome/#tt-console"/>
    <updated>2026-05-11T23:59:25Z</updated>
    <summary type="html"><![CDATA[Browser-based cloud console for exploring AI on Tenstorrent hardware. Run LLM inference, image and video generation, and browse the supported model catalog in-browser — backed by Tenstorrent accelerators. Cloud hardware access and advanced workflows (deployments, agents) available in staged rollout.]]></summary>
    <content type="html"><![CDATA[<p>Browser-based cloud console for exploring AI on Tenstorrent hardware. Run LLM inference, image and video generation, and browse the supported model catalog in-browser — backed by Tenstorrent accelerators. Cloud hardware access and advanced workflows (deployments, agents) available in staged rollout.</p>

<p><strong>Links:</strong> <a href="https://console.tenstorrent.com">console.tenstorrent.com</a></p>
<p>official · added 2026-05-11</p>
<p><em>cloud, console, inference, playground, llm, image-generation, video-generation, demo, cloud-infra, ai-models</em></p>]]></content>
    <category term="official"/>
    <category term="cloud-infra"/>
    <category term="ai-models"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-metal</id>
    <title>tt-metal</title>
    <link href="https://github.com/tenstorrent/tt-metal"/>
    <updated>2026-05-08T23:59:24Z</updated>
    <summary type="html"><![CDATA[TT-NN operator library and TT-Metalium low-level kernel programming model. The primary SDK for developing on Tenstorrent hardware — from high-level tensor ops to bare-metal RISC-V kernels.]]></summary>
    <content type="html"><![CDATA[<p>TT-NN operator library and TT-Metalium low-level kernel programming model. The primary SDK for developing on Tenstorrent hardware — from high-level tensor ops to bare-metal RISC-V kernels.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-metal">Repo</a> · <a href="https://docs.tenstorrent.com/tt-metal/latest/ttnn/">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>metalium, ttnn, sdk, kernels, core, kernels, compilers, getting-started</em></p>]]></content>
    <category term="official"/>
    <category term="kernels"/>
    <category term="compilers"/>
    <category term="getting-started"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-forge</id>
    <title>tt-forge</title>
    <link href="https://github.com/tenstorrent/tt-forge"/>
    <updated>2026-05-08T23:59:23Z</updated>
    <summary type="html"><![CDATA[Tenstorrent's MLIR-based compiler frontend. Enables running AI workloads from PyTorch, ONNX, and other frameworks on all Tenstorrent hardware configurations through an open-source, general, and performant compiler.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent's MLIR-based compiler frontend. Enables running AI workloads from PyTorch, ONNX, and other frameworks on all Tenstorrent hardware configurations through an open-source, general, and performant compiler.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-forge">Repo</a> · <a href="https://tenstorrent.com">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>mlir, compiler, pytorch, onnx, frontend, compilers, getting-started</em></p>]]></content>
    <category term="official"/>
    <category term="compilers"/>
    <category term="getting-started"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-buda</id>
    <title>tt-buda</title>
    <link href="https://github.com/tenstorrent/tt-buda"/>
    <updated>2026-05-08T23:59:22Z</updated>
    <summary type="html"><![CDATA[TT-BUDA: Tenstorrent's original Python compiler and runtime for AI workloads. Legacy stack — tt-forge is the recommended successor, but tt-buda has the largest model demo library.]]></summary>
    <content type="html"><![CDATA[<p>TT-BUDA: Tenstorrent's original Python compiler and runtime for AI workloads. Legacy stack — tt-forge is the recommended successor, but tt-buda has the largest model demo library.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-buda">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>legacy, compiler, pytorch, buda, compilers</em></p>]]></content>
    <category term="official"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-mlir</id>
    <title>tt-mlir</title>
    <link href="https://github.com/tenstorrent/tt-mlir"/>
    <updated>2026-05-08T23:59:21Z</updated>
    <summary type="html"><![CDATA[Tenstorrent MLIR compiler — the core compiler infrastructure shared by tt-forge and other frontends. Handles graph optimization, lowering, and code generation for Tensix hardware.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent MLIR compiler — the core compiler infrastructure shared by tt-forge and other frontends. Handles graph optimization, lowering, and code generation for Tensix hardware.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-mlir">Repo</a> · <a href="https://tenstorrent.github.io/tt-mlir/">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>mlir, compiler, backend, optimization, compilers</em></p>]]></content>
    <category term="official"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/riscv-ocelot</id>
    <title>riscv-ocelot</title>
    <link href="https://github.com/tenstorrent/riscv-ocelot"/>
    <updated>2026-05-08T23:59:20Z</updated>
    <summary type="html"><![CDATA[The Berkeley Out-of-Order Machine with V-EXT (RISC-V Vector Extension) support. Tenstorrent's research-grade out-of-order RISC-V core with vector extension.]]></summary>
    <content type="html"><![CDATA[<p>The Berkeley Out-of-Order Machine with V-EXT (RISC-V Vector Extension) support. Tenstorrent's research-grade out-of-order RISC-V core with vector extension.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/riscv-ocelot">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>risc-v, out-of-order, vector-extension, processor-design, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/ttsim</id>
    <title>ttsim</title>
    <link href="https://github.com/tenstorrent/ttsim"/>
    <updated>2026-05-08T23:59:19Z</updated>
    <summary type="html"><![CDATA[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.]]></summary>
    <content type="html"><![CDATA[<p>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.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/ttsim">Repo</a> · <a href="https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/ttsim-twenty-and-ten/">Lesson</a></p>
<p>official · added 2026-05-08</p>
<p><em>simulator, no-hardware, bit-exact, wormhole, blackhole, riscv-arch, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/whisper</id>
    <title>whisper</title>
    <link href="https://github.com/tenstorrent/whisper"/>
    <updated>2026-05-08T23:59:18Z</updated>
    <summary type="html"><![CDATA[RISC-V Instruction Set Simulator (ISS) used by Tenstorrent for processor verification. Powers the co-simulation architecture checker.]]></summary>
    <content type="html"><![CDATA[<p>RISC-V Instruction Set Simulator (ISS) used by Tenstorrent for processor verification. Powers the co-simulation architecture checker.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/whisper">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>risc-v, iss, simulator, verification, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-xla</id>
    <title>tt-xla</title>
    <link href="https://github.com/tenstorrent/tt-xla"/>
    <updated>2026-05-08T23:59:17Z</updated>
    <summary type="html"><![CDATA[PJRT device plugin for Tenstorrent hardware. Enables JAX, PyTorch/XLA, and other XLA-based frameworks to target TT accelerators.]]></summary>
    <content type="html"><![CDATA[<p>PJRT device plugin for Tenstorrent hardware. Enables JAX, PyTorch/XLA, and other XLA-based frameworks to target TT accelerators.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-xla">Repo</a> · <a href="https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/tt-xla-jax/">JAX and PyTorch/XLA on Tenstorrent</a> · <a href="https://docs.tenstorrent.com/tt-xla">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>xla, pjrt, jax, pytorch, compilers</em></p>]]></content>
    <category term="official"/>
    <category term="compilers"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-kmd</id>
    <title>tt-kmd</title>
    <link href="https://github.com/tenstorrent/tt-kmd"/>
    <updated>2026-05-08T23:59:16Z</updated>
    <summary type="html"><![CDATA[Tenstorrent kernel module driver. The Linux kernel module required to interface with Tenstorrent PCIe accelerator cards.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent kernel module driver. The Linux kernel module required to interface with Tenstorrent PCIe accelerator cards.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-kmd">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>kernel-module, driver, linux, pcie, hw-system</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/riescue</id>
    <title>RiESCUE</title>
    <link href="https://github.com/tenstorrent/riescue"/>
    <updated>2026-05-08T23:59:15Z</updated>
    <summary type="html"><![CDATA[RISC-V Directed Test Framework and Compliance Suite. Comprehensive test infrastructure for verifying RISC-V processor implementations against the specification.]]></summary>
    <content type="html"><![CDATA[<p>RISC-V Directed Test Framework and Compliance Suite. Comprehensive test infrastructure for verifying RISC-V processor implementations against the specification.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/riescue">Repo</a> · <a href="https://docs.tenstorrent.com/riescue/">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>risc-v, testing, compliance, verification, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-inference-server</id>
    <title>tt-inference-server</title>
    <link href="https://github.com/tenstorrent/tt-inference-server"/>
    <updated>2026-05-08T23:59:14Z</updated>
    <summary type="html"><![CDATA[Production-ready model serving for Tenstorrent hardware with OpenAI-compatible REST API. Supports continuous batching, multiple models, and all TT hardware configurations.]]></summary>
    <content type="html"><![CDATA[<p>Production-ready model serving for Tenstorrent hardware with OpenAI-compatible REST API. Supports continuous batching, multiple models, and all TT hardware configurations.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-inference-server">Repo</a> · <a href="https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/tt-inference-server-lesson/">Production Inference lesson (VSCode Toolkit)</a></p>
<p>official · added 2026-05-08</p>
<p><em>serving, openai-compatible, production, rest-api, ai-models, cloud-infra, getting-started</em></p>]]></content>
    <category term="official"/>
    <category term="ai-models"/>
    <category term="cloud-infra"/>
    <category term="getting-started"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-buda-demos</id>
    <title>tt-buda-demos</title>
    <link href="https://github.com/tenstorrent/tt-buda-demos"/>
    <updated>2026-05-08T23:59:13Z</updated>
    <summary type="html"><![CDATA[Repository of model demos using TT-Buda. The largest collection of pre-compiled model examples for Tenstorrent hardware — BERT, ResNet, YOLO, GPT-2, Whisper, and many more.]]></summary>
    <content type="html"><![CDATA[<p>Repository of model demos using TT-Buda. The largest collection of pre-compiled model examples for Tenstorrent hardware — BERT, ResNet, YOLO, GPT-2, Whisper, and many more.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-buda-demos">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>demos, models, bert, resnet, yolo, gpt2, ai-models</em></p>]]></content>
    <category term="official"/>
    <category term="ai-models"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-smi</id>
    <title>tt-smi</title>
    <link href="https://github.com/tenstorrent/tt-smi"/>
    <updated>2026-05-08T23:59:12Z</updated>
    <summary type="html"><![CDATA[Tenstorrent System Management Interface — monitor device telemetry, issue board-level resets, and inspect hardware health. The nvidia-smi equivalent for Tenstorrent hardware.]]></summary>
    <content type="html"><![CDATA[<p>Tenstorrent System Management Interface — monitor device telemetry, issue board-level resets, and inspect hardware health. The nvidia-smi equivalent for Tenstorrent hardware.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-smi">Repo</a></p>
<p>official · added 2026-05-08</p>
<p><em>monitoring, telemetry, smi, hardware-management, hw-system, dev-tools</em></p>]]></content>
    <category term="official"/>
    <category term="hw-system"/>
    <category term="dev-tools"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-bh-linux</id>
    <title>tt-bh-linux</title>
    <link href="https://github.com/tenstorrent/tt-bh-linux"/>
    <updated>2026-05-08T23:59:11Z</updated>
    <summary type="html"><![CDATA[Linux demo for the Tenstorrent Blackhole P100/P150 card RISC-V cores. Boot a real Linux kernel on the 16 high-performance RISC-V cores built into the Blackhole chip.]]></summary>
    <content type="html"><![CDATA[<p>Linux demo for the Tenstorrent Blackhole P100/P150 card RISC-V cores. Boot a real Linux kernel on the 16 high-performance RISC-V cores built into the Blackhole chip.</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-bh-linux">Repo</a> · <a href="https://tenstorrent.com/hardware/blackhole">Website</a></p>
<p>official · added 2026-05-08</p>
<p><em>linux, risc-v, blackhole, bare-metal, boot, riscv-arch</em></p>]]></content>
    <category term="official"/>
    <category term="riscv-arch"/>
  </entry>
  <entry>
    <id>https://github.com/tenstorrent/tt-lang</id>
    <title>tt-lang</title>
    <link href="https://github.com/tenstorrent/tt-lang"/>
    <updated>2026-05-08T23:59:10Z</updated>
    <summary type="html"><![CDATA[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).]]></summary>
    <content type="html"><![CDATA[<p>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).</p>

<p><strong>Links:</strong> <a href="https://github.com/tenstorrent/tt-lang">Repo</a> · <a href="https://docs.tenstorrent.com/tt-lang/">Website</a> · <a href="https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/tt-lang-intro/">Introduction to tt-lang</a></p>
<p>official · added 2026-05-08</p>
<p><em>dsl, python, kernels, tt-lang, simulator, kernel-fusion, kernels, getting-started</em></p>]]></content>
    <category term="official"/>
    <category term="kernels"/>
    <category term="getting-started"/>
  </entry>
</feed>
