{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "tt-awesome",
  "home_page_url": "https://tenstorrent.github.io/tt-awesome/",
  "feed_url": "https://tenstorrent.github.io/tt-awesome/feeds/feed.json",
  "description": "Projects, releases, articles, and resources from the Tenstorrent ecosystem",
  "items": [
    {
      "id": "https://github.com/tenstorrent/ttsim/releases/tag/v1.8.0",
      "url": "https://github.com/tenstorrent/ttsim/releases/tag/v1.8.0",
      "title": "ttsim v1.8.0",
      "summary": "ttsim released v1.8.0. Repository: https://github.com/tenstorrent/ttsim",
      "date_published": "2026-06-09T17:23:20Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-kmd/releases/tag/ttkmd-2.9.0",
      "url": "https://github.com/tenstorrent/tt-kmd/releases/tag/ttkmd-2.9.0",
      "title": "tt-kmd ttkmd-2.9.0",
      "summary": "tt-kmd released ttkmd-2.9.0. Repository: https://github.com/tenstorrent/tt-kmd",
      "date_published": "2026-06-09T13:25:19Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-metal/releases/tag/v0.72.0",
      "url": "https://github.com/tenstorrent/tt-metal/releases/tag/v0.72.0",
      "title": "tt-metal v0.72.0",
      "summary": "tt-metal released v0.72.0. Repository: https://github.com/tenstorrent/tt-metal",
      "date_published": "2026-06-09T01:30:48Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-forge/releases/tag/1.3.0.dev20260609002802",
      "url": "https://github.com/tenstorrent/tt-forge/releases/tag/1.3.0.dev20260609002802",
      "title": "tt-forge 1.3.0.dev20260609002802",
      "summary": "tt-forge released 1.3.0.dev20260609002802. Repository: https://github.com/tenstorrent/tt-forge",
      "date_published": "2026-06-09T01:16:05Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-xla/releases/tag/1.3.0.dev20260609002802",
      "url": "https://github.com/tenstorrent/tt-xla/releases/tag/1.3.0.dev20260609002802",
      "title": "tt-xla 1.3.0.dev20260609002802",
      "summary": "tt-xla released 1.3.0.dev20260609002802. Repository: https://github.com/tenstorrent/tt-xla",
      "date_published": "2026-06-09T01:07:31Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-toplike/releases/tag/v0.6.2",
      "url": "https://github.com/tenstorrent/tt-toplike/releases/tag/v0.6.2",
      "title": "tt-toplike v0.6.2",
      "summary": "tt-toplike released v0.6.2. Repository: https://github.com/tenstorrent/tt-toplike",
      "date_published": "2026-06-08T19:42:59Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-vscode-toolkit/releases/tag/v0.0.454",
      "url": "https://github.com/tenstorrent/tt-vscode-toolkit/releases/tag/v0.0.454",
      "title": "tt-vscode-toolkit v0.0.454",
      "summary": "tt-vscode-toolkit released v0.0.454. Repository: https://github.com/tenstorrent/tt-vscode-toolkit",
      "date_published": "2026-06-05T18:44:37Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-animatediff/releases/tag/v0.1.0",
      "url": "https://github.com/tenstorrent/tt-animatediff/releases/tag/v0.1.0",
      "title": "tt-animatediff v0.1.0",
      "summary": "tt-animatediff released v0.1.0. Repository: https://github.com/tenstorrent/tt-animatediff",
      "date_published": "2026-06-04T22:31:14Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/dstackai/dstack/releases/tag/0.20.23",
      "url": "https://github.com/dstackai/dstack/releases/tag/0.20.23",
      "title": "dstack 0.20.23",
      "summary": "dstack released 0.20.23. Repository: https://github.com/dstackai/dstack",
      "date_published": "2026-06-04T10:20:34Z",
      "tags": [
        "community",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/ttnn-visualizer/releases/tag/v0.88.0",
      "url": "https://github.com/tenstorrent/ttnn-visualizer/releases/tag/v0.88.0",
      "title": "ttnn-visualizer v0.88.0",
      "summary": "ttnn-visualizer released v0.88.0. Repository: https://github.com/tenstorrent/ttnn-visualizer",
      "date_published": "2026-06-03T20:23:29Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-animatediff",
      "url": "https://github.com/tenstorrent/tt-animatediff",
      "title": "tt-animatediff",
      "summary": "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.",
      "date_published": "2026-06-03T00:00:00Z",
      "tags": [
        "ai-models",
        "games-demos",
        "official",
        "entry"
      ]
    },
    {
      "id": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/animatediff-video-generation/",
      "url": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/animatediff-video-generation/",
      "title": "tt-animatediff — Native Video Animation with AnimateDiff (VSCode Toolkit)",
      "summary": "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.",
      "date_published": "2026-06-03T00:00:00Z",
      "tags": [
        "ai-models",
        "games-demos",
        "official",
        "lesson",
        "article"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-flash/releases/tag/v3.8.0",
      "url": "https://github.com/tenstorrent/tt-flash/releases/tag/v3.8.0",
      "title": "tt-flash v3.8.0",
      "summary": "tt-flash released v3.8.0. Repository: https://github.com/tenstorrent/tt-flash",
      "date_published": "2026-06-01T18:04:27Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-system-firmware/releases/tag/v19.10.0",
      "url": "https://github.com/tenstorrent/tt-system-firmware/releases/tag/v19.10.0",
      "title": "tt-system-firmware v19.10.0",
      "summary": "tt-system-firmware released v19.10.0. Repository: https://github.com/tenstorrent/tt-system-firmware",
      "date_published": "2026-06-01T13:21:59Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-inference-server/releases/tag/v0.15.0",
      "url": "https://github.com/tenstorrent/tt-inference-server/releases/tag/v0.15.0",
      "title": "tt-inference-server v0.15.0",
      "summary": "tt-inference-server released v0.15.0. Repository: https://github.com/tenstorrent/tt-inference-server",
      "date_published": "2026-05-29T15:55:11Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/Zaneham/BarraCUDA/releases/tag/v0.5.0",
      "url": "https://github.com/Zaneham/BarraCUDA/releases/tag/v0.5.0",
      "title": "BarraCUDA v0.5.0",
      "summary": "BarraCUDA released v0.5.0. Repository: https://github.com/Zaneham/BarraCUDA",
      "date_published": "2026-05-29T04:30:28Z",
      "tags": [
        "community",
        "release"
      ]
    },
    {
      "id": "https://github.com/Zaneham/ttas/releases/tag/v0.1.0",
      "url": "https://github.com/Zaneham/ttas/releases/tag/v0.1.0",
      "title": "ttas v0.1.0",
      "summary": "ttas released v0.1.0. Repository: https://github.com/Zaneham/ttas",
      "date_published": "2026-05-28T07:08:35Z",
      "tags": [
        "community",
        "release"
      ]
    },
    {
      "id": "https://github.com/Zaneham/ttas",
      "url": "https://github.com/Zaneham/ttas",
      "title": "ttas",
      "summary": "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.",
      "date_published": "2026-05-27T00:00:00Z",
      "tags": [
        "dev-tools",
        "hw-system",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-local-generator/releases/tag/v0.3.4",
      "url": "https://github.com/tenstorrent/tt-local-generator/releases/tag/v0.3.4",
      "title": "tt-local-generator v0.3.4",
      "summary": "tt-local-generator released v0.3.4. Repository: https://github.com/tenstorrent/tt-local-generator",
      "date_published": "2026-05-26T23:52:15Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-studio/releases/tag/v2.6.0",
      "url": "https://github.com/tenstorrent/tt-studio/releases/tag/v2.6.0",
      "title": "TT-Studio v2.6.0",
      "summary": "TT-Studio released v2.6.0. Repository: https://github.com/tenstorrent/tt-studio",
      "date_published": "2026-05-20T17:04:32Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/Knight-Ops/libtt-metal-cxx",
      "url": "https://github.com/Knight-Ops/libtt-metal-cxx",
      "title": "libtt-metal-cxx",
      "summary": "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.",
      "date_published": "2026-05-20T00:00:00Z",
      "tags": [
        "dev-tools",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-smi/releases/tag/v5.2.0",
      "url": "https://github.com/tenstorrent/tt-smi/releases/tag/v5.2.0",
      "title": "tt-smi v5.2.0",
      "summary": "tt-smi released v5.2.0. Repository: https://github.com/tenstorrent/tt-smi",
      "date_published": "2026-05-14T17:26:26Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#blog-anuraagw-blackhole-arch",
      "url": "https://anuraagw.me/blog/blackhole-architecture",
      "title": "Tenstorrent Blackhole Architecture Guide",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "riscv-arch",
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://anuraagw.me/blog/blackhole-architecture",
      "url": "https://anuraagw.me/blog/blackhole-architecture",
      "title": "Tenstorrent Blackhole Architecture Guide — anuraagw.me — February 2026",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "riscv-arch",
        "guides",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#lecture-wm-csci654-tenstorrent",
      "url": "https://www.youtube.com/watch?v=CixEFPc8oxg",
      "title": "Tenstorrent Architecture — W&M CSCI654 Advanced Computer Architecture",
      "summary": "Lecture 20 from William & Mary's graduate Computer Architecture course. Frames Tenstorrent in the landscape between GPUs and TPUs, draws comparisons to Cerebras and SambaNova, then dives deep into the Wormhole chip and Tensix core: the 5 RISC-V core design, SFPU, NoC, and dataflow execution model.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "guides",
        "riscv-arch",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://www.youtube.com/watch?v=CixEFPc8oxg",
      "url": "https://www.youtube.com/watch?v=CixEFPc8oxg",
      "title": "Tenstorrent Architecture — W&M CSCI654 Advanced Computer Architecture — Lecture 20 — Tenstorrent Architecture (YouTube)",
      "summary": "Lecture 20 from William & Mary's graduate Computer Architecture course. Frames Tenstorrent in the landscape between GPUs and TPUs, draws comparisons to Cerebras and SambaNova, then dives deep into the Wormhole chip and Tensix core: the 5 RISC-V core design, SFPU, NoC, and dataflow execution model.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "guides",
        "riscv-arch",
        "community",
        "video",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-attention-grayskull",
      "url": "https://arxiv.org/abs/2407.13885",
      "title": "Attention in SRAM on Tenstorrent Grayskull",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2407.13885",
      "url": "https://arxiv.org/abs/2407.13885",
      "title": "Attention in SRAM on Tenstorrent Grayskull — arXiv:2407.13885",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "kernels",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-matmul-grayskull",
      "url": "https://arxiv.org/abs/2505.06085",
      "title": "Assessing Tenstorrent Grayskull RISC-V MatMul Acceleration for LLMs",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2505.06085",
      "url": "https://arxiv.org/abs/2505.06085",
      "title": "Assessing Tenstorrent Grayskull RISC-V MatMul Acceleration for LLMs — arXiv:2505.06085",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-nbody-strategies-wormhole",
      "url": "https://arxiv.org/abs/2605.02744",
      "title": "Porting Strategies for Gravitational N-Body Simulations on Tenstorrent Wormhole",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2605.02744",
      "url": "https://arxiv.org/abs/2605.02744",
      "title": "Porting Strategies for Gravitational N-Body Simulations on Tenstorrent Wormhole — arXiv:2605.02744",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-tileloom",
      "url": "https://arxiv.org/abs/2512.22168",
      "title": "TileLoom: Automatic Dataflow Planning for Spatial Dataflow Accelerators",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "compilers",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2512.22168",
      "url": "https://arxiv.org/abs/2512.22168",
      "title": "TileLoom: Automatic Dataflow Planning for Spatial Dataflow Accelerators — arXiv:2512.22168",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "compilers",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-tts-lightning",
      "url": "https://arxiv.org/abs/2604.03279",
      "title": "Rewriting TTS Inference Economics: Lightning V2 on Tenstorrent vs. NVIDIA L40S",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "ai-models",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2604.03279",
      "url": "https://arxiv.org/abs/2604.03279",
      "title": "Rewriting TTS Inference Economics: Lightning V2 on Tenstorrent vs. NVIDIA L40S — arXiv:2604.03279",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "research",
        "ai-models",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-llk",
      "url": "https://github.com/tenstorrent/tt-llk",
      "title": "tt-llk",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "kernels",
        "riscv-arch",
        "official",
        "entry"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-llk/blob/main/docs/llk/l2/top_level_overview.md",
      "url": "https://github.com/tenstorrent/tt-llk/blob/main/docs/llk/l2/top_level_overview.md",
      "title": "tt-llk — Top-level architecture overview",
      "summary": "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.",
      "date_published": "2026-05-13T00:00:00Z",
      "tags": [
        "kernels",
        "riscv-arch",
        "official",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-stencils-wormhole",
      "url": "https://arxiv.org/abs/2605.07599",
      "title": "Stencil Computations on Tenstorrent Wormhole",
      "summary": "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.",
      "date_published": "2026-05-12T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2605.07599",
      "url": "https://arxiv.org/abs/2605.07599",
      "title": "Stencil Computations on Tenstorrent Wormhole — arXiv:2605.07599",
      "summary": "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.",
      "date_published": "2026-05-12T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/whisper/releases/tag/1.861",
      "url": "https://github.com/tenstorrent/whisper/releases/tag/1.861",
      "title": "whisper 1.861",
      "summary": "whisper released 1.861. Repository: https://github.com/tenstorrent/whisper",
      "date_published": "2026-05-11T15:44:36Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/mesham/tt-sim/releases/tag/v1.0",
      "url": "https://github.com/mesham/tt-sim/releases/tag/v1.0",
      "title": "tt-sim v1.0",
      "summary": "tt-sim released v1.0. Repository: https://github.com/mesham/tt-sim",
      "date_published": "2026-05-11T13:07:42Z",
      "tags": [
        "community",
        "release"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#tt-console",
      "url": "https://console.tenstorrent.com",
      "title": "TT Console",
      "summary": "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.",
      "date_published": "2026-05-11T00:00:00Z",
      "tags": [
        "cloud-infra",
        "ai-models",
        "official",
        "entry"
      ]
    },
    {
      "id": "https://github.com/Syllo/nvtop",
      "url": "https://github.com/Syllo/nvtop",
      "title": "nvtop",
      "summary": "htop-style process monitor for GPUs and AI accelerators. Supports AMD, Apple, Huawei, Intel, NVIDIA, Qualcomm — and Tenstorrent. Real-time utilization, memory, and process info in a terminal UI.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "dev-tools",
        "hw-system",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/dstackai/dstack",
      "url": "https://github.com/dstackai/dstack",
      "title": "dstack",
      "summary": "Vendor-agnostic orchestration for training, inference, and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "cloud-infra",
        "agents",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/Zaneham/BarraCUDA",
      "url": "https://github.com/Zaneham/BarraCUDA",
      "title": "BarraCUDA",
      "summary": "Open-source CUDA compiler targeting multiple GPU architectures including Tenstorrent. Compiles .cu files to run on AMD and Tenstorrent hardware without modification.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "compilers",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/geohot/tt-tiny",
      "url": "https://github.com/geohot/tt-tiny",
      "title": "tt-tiny",
      "summary": "Minimal Python code to access and program the Tenstorrent Blackhole chip directly — George Hotz's exploration of TT hardware programmability with pointed commentary on the architecture.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/mesham/tt-sim",
      "url": "https://github.com/mesham/tt-sim",
      "title": "tt-sim",
      "summary": "Community-built Tenstorrent architecture simulator written in Python. Runs without hardware — useful for researchers and developers exploring the Tensix architecture offline.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "riscv-arch",
        "dev-tools",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/swote-git/tt-iree",
      "url": "https://github.com/swote-git/tt-iree",
      "title": "tt-iree",
      "summary": "IREE (Intermediate Representation Execution Environment) ML compiler ported to Tenstorrent AI accelerators. Brings the IREE compiler ecosystem to TT hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "compilers",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/kernelize-ai/triton-tenstorrent",
      "url": "https://github.com/kernelize-ai/triton-tenstorrent",
      "title": "triton-tenstorrent",
      "summary": "OpenAI Triton compiler plugin for Tenstorrent hardware. Write Triton kernels and target Tensix cores — brings the Triton ML kernel ecosystem to TT devices.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "compilers",
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/olofj/bhx",
      "url": "https://github.com/olofj/bhx",
      "title": "bhx",
      "summary": "Boot stock Linux cloud images on the SiFive X280 RISC-V cores inside Tenstorrent Blackhole AI accelerators. Per-card Rust daemon with virtio-mmio block/net/console and U-Boot/EFI support.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "riscv-arch",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-bio",
      "url": "https://github.com/tenstorrent/tt-bio",
      "title": "tt-boltz",
      "summary": "Boltz-2 biomolecular model for drug discovery on Tenstorrent Blackhole. Supports single-card and multi-card configurations — QuietBox (4×) and Galaxy (32×). Approaches physics-based FEP accuracy at 1000× the speed.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "ai-models",
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://fosdem.org/2026/schedule/event/AJLNVH-tt-boltz/",
      "url": "https://fosdem.org/2026/schedule/event/AJLNVH-tt-boltz/",
      "title": "tt-boltz — FOSDEM 2026 — Drug Discovery on Tenstorrent Hardware",
      "summary": "Boltz-2 biomolecular model for drug discovery on Tenstorrent Blackhole. Supports single-card and multi-card configurations — QuietBox (4×) and Galaxy (32×). Approaches physics-based FEP accuracy at 1000× the speed.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "ai-models",
        "research",
        "community",
        "talk",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#blog-martin-chang-programming",
      "url": "https://clehaxze.tw/gemlog/2025/04-21-programming-tensotrrent-processors.gmi",
      "title": "Programming Tenstorrent Processors",
      "summary": "Deep-dive into the Tenstorrent architecture and Metalium programming model — circular buffers, kernel synchronization, NoC routing, and where the footguns are. The honest guide to thinking in Tensix.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "kernels",
        "riscv-arch",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://clehaxze.tw/gemlog/2025/04-21-programming-tensotrrent-processors.gmi",
      "url": "https://clehaxze.tw/gemlog/2025/04-21-programming-tensotrrent-processors.gmi",
      "title": "Programming Tenstorrent Processors — clehaxze.tw — April 2025",
      "summary": "Deep-dive into the Tenstorrent architecture and Metalium programming model — circular buffers, kernel synchronization, NoC routing, and where the footguns are. The honest guide to thinking in Tensix.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "kernels",
        "riscv-arch",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#jasondavies-tt-series",
      "url": "https://www.jasondavies.com/2025/tenstorrent-where/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2025/tenstorrent-where/",
      "url": "https://www.jasondavies.com/2025/tenstorrent-where/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — Optimal \"where\" on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2025/tenstorrent-multiply-int32/",
      "url": "https://www.jasondavies.com/2025/tenstorrent-multiply-int32/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — 32-bit Integer Multiplication on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2025/tenstorrent-typecast/",
      "url": "https://www.jasondavies.com/2025/tenstorrent-typecast/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — Typecast on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2026/tenstorrent-multiply-int16/",
      "url": "https://www.jasondavies.com/2026/tenstorrent-multiply-int16/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — 16-bit Integer Multiplication on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2026/tenstorrent-cbrt/",
      "url": "https://www.jasondavies.com/2026/tenstorrent-cbrt/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — Cube Root on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://www.jasondavies.com/2026/tenstorrent-sin-cos-tan/",
      "url": "https://www.jasondavies.com/2026/tenstorrent-sin-cos-tan/",
      "title": "Tenstorrent SFPU Kernel Series — Jason Davies — Accurate sin/cos/tan on Tenstorrent",
      "summary": "Sponsored series of deep technical articles on implementing optimal SFPU kernels for the Tenstorrent Wormhole and Blackhole vector units. Covers where, typecasting, 16/32-bit integer multiplication, cube root, and accurate sin/cos/tan — with cycle counts, assembly walkthroughs, and Blackhole vs Wormhole comparisons throughout.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://github.com/moritztng/grayskull-attention",
      "url": "https://github.com/moritztng/grayskull-attention",
      "title": "grayskull-attention",
      "summary": "FlashAttention-style attention kernel implemented entirely in on-chip SRAM on the Tenstorrent Grayskull chip using TT-Metalium. Pioneering work in low-level attention on TT hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "ai-models",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/geohot/tt-twitch",
      "url": "https://github.com/geohot/tt-twitch",
      "title": "tt-twitch",
      "summary": "A Tenstorrent Grayskull kernel written live on Twitch by George Hotz. 120-core grid demonstration of live kernel programming.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "games-demos",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/koyeb/tenstorrent-examples",
      "url": "https://github.com/koyeb/tenstorrent-examples",
      "title": "koyeb/tenstorrent-examples",
      "summary": "Example applications and deployment configurations for running AI workloads on Tenstorrent hardware via Koyeb's cloud platform.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "cloud-infra",
        "ai-models",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/boopdotpng/blackhole-py",
      "url": "https://github.com/boopdotpng/blackhole-py",
      "title": "blackhole-py",
      "summary": "Pure Python driver for Tenstorrent Blackhole cards providing direct low-level hardware access without going through the full TT-Metal stack.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "hw-system",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/jaebaek/tenstorrent-tiny-examples",
      "url": "https://github.com/jaebaek/tenstorrent-tiny-examples",
      "title": "tenstorrent-tiny-examples",
      "summary": "Simple C++ kernel experiments on a GraySkull e75 chip. Hands-on examples for learning the TT-Metal programming model at the metal level.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/marty1885/ttnn-helloworld-cpp",
      "url": "https://github.com/marty1885/ttnn-helloworld-cpp",
      "title": "ttnn-helloworld-cpp",
      "summary": "Minimal working example of using Tenstorrent TTNN in C++. The simplest possible starting point for C++ developers targeting TT hardware with TTNN.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/JushBJJ/TT-GoL",
      "url": "https://github.com/JushBJJ/TT-GoL",
      "title": "TT-GoL",
      "summary": "Conway's Game of Life implemented on Tenstorrent hardware using TT-Metal kernels.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "games-demos",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/marty1885/ttMandelbrot",
      "url": "https://github.com/marty1885/ttMandelbrot",
      "title": "ttMandelbrot",
      "summary": "Mandelbrot Set fractal renderer running on Tenstorrent hardware. A classic demo showcasing parallel compute on Tensix cores.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "games-demos",
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/JushBJJ/TT-Metal-Mini-Template",
      "url": "https://github.com/JushBJJ/TT-Metal-Mini-Template",
      "title": "TT-Metal Mini Template",
      "summary": "Minimal working CMake project template for starting a new TT-Metal project from scratch. Good starting point for community kernel development.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/RISCVtestbed/tt-tutorial",
      "url": "https://github.com/RISCVtestbed/tt-tutorial",
      "title": "tt-tutorial (HPC)",
      "summary": "Tutorial on Tenstorrent hardware for HPC researchers from the RISC-V Testbed project at Edinburgh/EPCC. Covers Wormhole from an HPC parallel-computing perspective.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/TT-Bounty-Hunters/ttPEAK",
      "url": "https://github.com/TT-Bounty-Hunters/ttPEAK",
      "title": "ttPEAK",
      "summary": "clpeak-style peak-performance benchmark for Tenstorrent devices using TT-Metalium. Measures theoretical peak throughput across operations — useful for hardware characterization.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "dev-tools",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/RossComputerGuy/tenstorrent.nix",
      "url": "https://github.com/RossComputerGuy/tenstorrent.nix",
      "title": "tenstorrent.nix",
      "summary": "Nix flake packaging the Tenstorrent software stack for NixOS and Nix users. Reproducible, declarative installation of TT drivers and tools.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "hw-system",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/seansiddens/current",
      "url": "https://github.com/seansiddens/current",
      "title": "current",
      "summary": "High-level parallel programming framework for Tenstorrent accelerators, abstracting TT-Metal into a research-oriented programming model for parallel computation.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/marty1885/ttVecAdd",
      "url": "https://github.com/marty1885/ttVecAdd",
      "title": "ttVecAdd",
      "summary": "Minimal vector-addition example on Tenstorrent devices using TT-Metalium. A clean hello-world for the TT-Metal kernel programming model in C++.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "kernels",
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/changh95/tt-tutorial",
      "url": "https://github.com/changh95/tt-tutorial",
      "title": "tt-tutorial (Korean)",
      "summary": "Comprehensive tutorials for the Tenstorrent software stack in Korean. Jupyter notebooks covering the full developer path from hardware setup to model inference.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/EngineerCharlie/TenstorrentAllreduce",
      "url": "https://github.com/EngineerCharlie/TenstorrentAllreduce",
      "title": "Collective Operations on Wormhole n150 (Sapienza University of Rome)",
      "summary": "Master's thesis implementing and benchmarking five allreduce algorithms (Swing, Recursive Doubling, Bandwidth Optimal, Latency Optimal, Shared Memory) on the Wormhole n150. Bandwidth Optimal achieved best performance, approaching within 2× of theoretical optimal.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://github.com/Kovelja009/gsplat_tt",
      "url": "https://github.com/Kovelja009/gsplat_tt",
      "title": "gsplat_tt",
      "summary": "Port of Gaussian Splatting (3D scene reconstruction from 2D images) to Tenstorrent hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "ai-models",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#blog-martin-chang-arch-linux",
      "url": "https://clehaxze.tw/gemlog/2024/07-07-a-gentle-guide-on-getting-your-tenstorrent-card-running-on-arch-linux-with-the-metalium-stack.gmi",
      "title": "A Gentle Guide: Tenstorrent Card on Arch Linux with Metalium",
      "summary": "Step-by-step guide to getting a Tenstorrent card running on Arch Linux with the full Metalium stack. Practical troubleshooting from someone who did it the hard way first.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://clehaxze.tw/gemlog/2024/07-07-a-gentle-guide-on-getting-your-tenstorrent-card-running-on-arch-linux-with-the-metalium-stack.gmi",
      "url": "https://clehaxze.tw/gemlog/2024/07-07-a-gentle-guide-on-getting-your-tenstorrent-card-running-on-arch-linux-with-the-metalium-stack.gmi",
      "title": "A Gentle Guide: Tenstorrent Card on Arch Linux with Metalium — clehaxze.tw — July 2024",
      "summary": "Step-by-step guide to getting a Tenstorrent card running on Arch Linux with the full Metalium stack. Practical troubleshooting from someone who did it the hard way first.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#blog-martin-chang-grayskull",
      "url": "https://clehaxze.tw/gemlog/2024/06-02-thoughts-and-logs-after-messing-with-tenstorrent-grayskull.gmi",
      "title": "Thoughts and Logs After Messing with Tenstorrent Grayskull",
      "summary": "Honest field notes from getting a Grayskull card running and writing first Metalium kernels. Covers setup pitfalls, processor hangs, memory protection quirks, and what makes Metalium compelling despite early rough edges.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://clehaxze.tw/gemlog/2024/06-02-thoughts-and-logs-after-messing-with-tenstorrent-grayskull.gmi",
      "url": "https://clehaxze.tw/gemlog/2024/06-02-thoughts-and-logs-after-messing-with-tenstorrent-grayskull.gmi",
      "title": "Thoughts and Logs After Messing with Tenstorrent Grayskull — clehaxze.tw — June 2024",
      "summary": "Honest field notes from getting a Grayskull card running and writing first Metalium kernels. Covers setup pitfalls, processor hangs, memory protection quirks, and what makes Metalium compelling despite early rough edges.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "guides",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-fft-wormhole",
      "url": "https://arxiv.org/abs/2506.15437",
      "title": "Exploring Fast Fourier Transforms on the Tenstorrent Wormhole",
      "summary": "Ports the Cooley-Tukey FFT algorithm to the Wormhole n300 RISC-V accelerator. The Wormhole draws 8× less power and consumes 2.8× less energy than a 24-core Xeon Platinum for a 2D FFT. ISC 2025.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "kernels",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2506.15437",
      "url": "https://arxiv.org/abs/2506.15437",
      "title": "Exploring Fast Fourier Transforms on the Tenstorrent Wormhole — arXiv:2506.15437",
      "summary": "Ports the Cooley-Tukey FFT algorithm to the Wormhole n300 RISC-V accelerator. The Wormhole draws 8× less power and consumes 2.8× less energy than a 24-core Xeon Platinum for a 2D FFT. ISC 2025.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "kernels",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://www.research.ed.ac.uk/en/publications/exploring-fast-fourier-transforms-on-the-tenstorrent-wormhole/",
      "url": "https://www.research.ed.ac.uk/en/publications/exploring-fast-fourier-transforms-on-the-tenstorrent-wormhole/",
      "title": "Exploring Fast Fourier Transforms on the Tenstorrent Wormhole — University of Edinburgh",
      "summary": "Ports the Cooley-Tukey FFT algorithm to the Wormhole n300 RISC-V accelerator. The Wormhole draws 8× less power and consumes 2.8× less energy than a 24-core Xeon Platinum for a 2D FFT. ISC 2025.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "kernels",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-nbody-wormhole",
      "url": "https://arxiv.org/abs/2509.19294",
      "title": "Accelerating Gravitational N-Body Simulations on Tenstorrent Wormhole",
      "summary": "Accelerates an astrophysical N-body simulation on the Wormhole n300. Achieves 2× speedup and 2× energy savings over a highly optimized CPU implementation. SC '25 Workshop.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2509.19294",
      "url": "https://arxiv.org/abs/2509.19294",
      "title": "Accelerating Gravitational N-Body Simulations on Tenstorrent Wormhole — arXiv:2509.19294",
      "summary": "Accelerates an astrophysical N-body simulation on the Wormhole n300. Achieves 2× speedup and 2× energy savings over a highly optimized CPU implementation. SC '25 Workshop.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://dl.acm.org/doi/10.1145/3731599.3767528",
      "url": "https://dl.acm.org/doi/10.1145/3731599.3767528",
      "title": "Accelerating Gravitational N-Body Simulations on Tenstorrent Wormhole — ACM SC '25",
      "summary": "Accelerates an astrophysical N-body simulation on the Wormhole n300. Achieves 2× speedup and 2× energy savings over a highly optimized CPU implementation. SC '25 Workshop.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "article",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-numerical-kernels-wormhole",
      "url": "https://arxiv.org/abs/2603.23343",
      "title": "Numerical Kernels on a Spatial Accelerator: Tenstorrent Wormhole",
      "summary": "Implements three numerical kernels and composes them into a conjugate gradient solver on Wormhole. Demonstrates AI accelerators merit consideration for HPC workloads traditionally dominated by CPUs and GPUs. 2026.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2603.23343",
      "url": "https://arxiv.org/abs/2603.23343",
      "title": "Numerical Kernels on a Spatial Accelerator: Tenstorrent Wormhole — arXiv:2603.23343",
      "summary": "Implements three numerical kernels and composes them into a conjugate gradient solver on Wormhole. Demonstrates AI accelerators merit consideration for HPC workloads traditionally dominated by CPUs and GPUs. 2026.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-stencils-grayskull",
      "url": "https://arxiv.org/abs/2409.18835",
      "title": "Accelerating Stencils on the Tenstorrent Grayskull RISC-V Accelerator",
      "summary": "Explores stencil computation on the Grayskull PCIe RISC-V accelerator. Early academic work examining TT hardware for HPC stencil workloads. 2024.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://arxiv.org/abs/2409.18835",
      "url": "https://arxiv.org/abs/2409.18835",
      "title": "Accelerating Stencils on the Tenstorrent Grayskull RISC-V Accelerator — arXiv:2409.18835",
      "summary": "Explores stencil computation on the Grayskull PCIe RISC-V accelerator. Early academic work examining TT hardware for HPC stencil workloads. 2024.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#paper-swiftnpu",
      "url": "https://dl.acm.org/doi/10.1145/3805621.3807614",
      "title": "SwiftNPU: Scalable Shape-Flexible Allocation for Inter-Core Connected NPUs",
      "summary": "Makes multi-tenant NPU sharing practical for Blackhole-class hardware using polynomial-time allocation algorithms. Delivers up to 1.37× higher utilization and 1.14× faster workload completion. Up to 890,000× faster than NP-hard baselines.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "hw-system",
        "community",
        "entry"
      ]
    },
    {
      "id": "https://dl.acm.org/doi/10.1145/3805621.3807614",
      "url": "https://dl.acm.org/doi/10.1145/3805621.3807614",
      "title": "SwiftNPU: Scalable Shape-Flexible Allocation for Inter-Core Connected NPUs — ACM DL",
      "summary": "Makes multi-tenant NPU sharing practical for Blackhole-class hardware using polynomial-time allocation algorithms. Delivers up to 1.37× higher utilization and 1.14× faster workload completion. Up to 890,000× faster than NP-hard baselines.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "research",
        "hw-system",
        "community",
        "paper",
        "article"
      ]
    },
    {
      "id": "https://github.com/tsingletaryTT/tt-zork-and-more",
      "url": "https://github.com/tsingletaryTT/tt-zork-and-more",
      "title": "tt-zork-and-more",
      "summary": "A Tenstorrent fork of Infocom's Zork I (and more!), running a Z-machine interpreter at least four different ways on TT hardware. The most fun you can have with an AI accelerator.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "games-demos",
        "affiliated",
        "entry"
      ]
    },
    {
      "id": "https://tenstorrent.github.io/tt-awesome/#ttvt-ai-agents",
      "url": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/qb2-local-agents/",
      "title": "Local AI Agents on Tenstorrent",
      "summary": "Three agentic projects running fully on-device: local AI agents on QuietBox 2, a coding assistant powered by Aider against a local inference server, and the OpenClaw AI assistant on QuietBox 2. No cloud APIs — all inference runs on TT hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "agents",
        "affiliated",
        "entry"
      ]
    },
    {
      "id": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/qb2-local-agents/",
      "url": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/qb2-local-agents/",
      "title": "Local AI Agents on Tenstorrent — Local AI Agents on QuietBox 2",
      "summary": "Three agentic projects running fully on-device: local AI agents on QuietBox 2, a coding assistant powered by Aider against a local inference server, and the OpenClaw AI assistant on QuietBox 2. No cloud APIs — all inference runs on TT hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "agents",
        "affiliated",
        "lesson",
        "article"
      ]
    },
    {
      "id": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/coding-assistant/",
      "url": "https://docs.tenstorrent.com/tt-vscode-toolkit/lessons/coding-assistant/",
      "title": "Local AI Agents on Tenstorrent — Coding Assistant with Aider",
      "summary": "Three agentic projects running fully on-device: local AI agents on QuietBox 2, a coding assistant powered by Aider against a local inference server, and the OpenClaw AI assistant on QuietBox 2. No cloud APIs — all inference runs on TT hardware.",
      "date_published": "2026-05-08T00:00:00Z",
      "tags": [
        "agents",
        "affiliated",
        "lesson",
        "article"
      ]
    },
    {
      "id": "https://github.com/tenstorrent-riscv-software/tt-bh-linux/releases/tag/v0.11",
      "url": "https://github.com/tenstorrent-riscv-software/tt-bh-linux/releases/tag/v0.11",
      "title": "tt-bh-linux v0.11",
      "summary": "tt-bh-linux released v0.11. Repository: https://github.com/tenstorrent/tt-bh-linux",
      "date_published": "2026-04-13T15:10:59Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/luwen/releases/tag/v0.8.5",
      "url": "https://github.com/tenstorrent/luwen/releases/tag/v0.8.5",
      "title": "luwen v0.8.5",
      "summary": "luwen released v0.8.5. Repository: https://github.com/tenstorrent/luwen",
      "date_published": "2026-03-30T21:03:56Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-installer/releases/tag/v2.2.1",
      "url": "https://github.com/tenstorrent/tt-installer/releases/tag/v2.2.1",
      "title": "tt-installer v2.2.1",
      "summary": "tt-installer released v2.2.1. Repository: https://github.com/tenstorrent/tt-installer",
      "date_published": "2026-03-16T18:54:29Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-topology/releases/tag/v1.2.19",
      "url": "https://github.com/tenstorrent/tt-topology/releases/tag/v1.2.19",
      "title": "tt-topology v1.2.19",
      "summary": "tt-topology released v1.2.19. Repository: https://github.com/tenstorrent/tt-topology",
      "date_published": "2026-02-26T21:14:41Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-firmware/releases/tag/v19.6.0",
      "url": "https://github.com/tenstorrent/tt-firmware/releases/tag/v19.6.0",
      "title": "tt-firmware v19.6.0",
      "summary": "tt-firmware released v19.6.0. Repository: https://github.com/tenstorrent/tt-firmware",
      "date_published": "2026-02-20T16:53:34Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/Syllo/nvtop/releases/tag/3.3.2",
      "url": "https://github.com/Syllo/nvtop/releases/tag/3.3.2",
      "title": "nvtop 3.3.2",
      "summary": "nvtop released 3.3.2. Repository: https://github.com/Syllo/nvtop",
      "date_published": "2026-02-08T17:57:16Z",
      "tags": [
        "community",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/riescue/releases/tag/v1.7.0",
      "url": "https://github.com/tenstorrent/riescue/releases/tag/v1.7.0",
      "title": "RiESCUE v1.7.0",
      "summary": "RiESCUE released v1.7.0. Repository: https://github.com/tenstorrent/riescue",
      "date_published": "2025-12-03T19:29:44Z",
      "tags": [
        "official",
        "release"
      ]
    },
    {
      "id": "https://github.com/tenstorrent/tt-buda/releases/tag/v0.19.3",
      "url": "https://github.com/tenstorrent/tt-buda/releases/tag/v0.19.3",
      "title": "tt-buda v0.19.3",
      "summary": "tt-buda released v0.19.3. Repository: https://github.com/tenstorrent/tt-buda",
      "date_published": "2024-09-24T21:01:08Z",
      "tags": [
        "official",
        "release"
      ]
    }
  ]
}