tenstorrent

    tenstorrent/tt-metal

    :metal: TT-NN operator library, and TT-Metalium low level kernel programming model.

    ai
    graphics
    llm
    accelerator
    cuda
    deepseek
    gpu
    img-gen
    Python
    Apache-2.0
    1.7K stars
    797 forks
    1.7K GitHub watchers
    Updated Oct 30, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest today, with 379 contributors on GitHub.

    Gained 32% fewer stars in the last 90 days than in the 90 days before.

    Use Cases & Benefits

    • Provides a Python and C++ neural network operator library and a low-level kernel programming model for Tenstorrent hardware.
    • Enables optimized kernel development and high-performance neural network operations tailored for Tenstorrent accelerators.
    • Use for developing and optimizing deep learning models on Tenstorrent GPU architectures with TT-NN operators.
    • Use for programming custom compute kernels and data movement operations using the TT-Metalium low-level model.
    • Use for benchmarking and improving performance of large language models and vision transformers on specialized hardware.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Apache 2.0

    Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.

    Community files

    • README: present
    • Contributing guide: present
    • Code of conduct: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    TT-NN is a Python & C++ Neural Network OP library.

    The Models team is focused on developing the following models to a customer-ready state. Ongoing work includes optimizations for performance, accuracy, and compatibility. Follow each model link for more details.

    Excerpt from the tenstorrent/tt-metal README on GitHub (Apache-2.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    797+247

    Open issues

    7.2K+1.4K

    Compared with peers in AI

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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