Showing 4 open source projects for "open dynamics engine"

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  • 1
    h3-metal

    h3-metal

    MiniMax H3 inference engine for Mac computers

    h3-metal is a native MiniMax-H3 inference engine for Apple Silicon focused on generating video and audio locally with Metal. It supports prompt-to-video and prompt-to-audio workflows as well as first-frame, last-frame, image, video, and audio references. An interactive terminal session keeps prompt conditioning, the diffusion transformer, and the video decoder in memory for faster repeated generations. Users can trade speed, quality, and memory through denoising steps, layer counts, reuse...
    Downloads: 3 This Week
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  • 2
    Kimi K3 in C

    Kimi K3 in C

    A 2.78-trillion-parameter Kimi K3 running inference on a single CPU

    Kimi K3 in C is a portable C99 inference engine built to run the 2.78-trillion-parameter Kimi K3 model on CPUs without BLAS, machine-learning frameworks, or GPUs. It demonstrates inference from a roughly 1.56 TB checkpoint with measured memory use as low as 8.24 GB. The runtime streams model trunk layers and routed experts from disk instead of keeping all weights resident in memory. Memory presets balance pinned layers and an expert LRU cache for laptops, desktops, workstations, and servers....
    Downloads: 12 This Week
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  • 3
    WASTE

    WASTE

    Run the full 2.78-trillion-parameter Kimi K3 model

    WASTE is an embeddable C inference engine for running extremely large mixture-of-experts models when the weights exceed available RAM. It keeps the shared model trunk in memory and streams only the experts selected for each token from fast NVMe storage. A bounded cache reuses recently needed experts, while lookahead routing begins disk reads before the next layer requires them. Its main target is the full 2.78-trillion-parameter Kimi K3 model, including multimodal image input. The engine has...
    Downloads: 2 This Week
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  • 4
    Colibrì

    Colibrì

    Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine

    Colibri is a compact inference engine designed to run the 744-billion-parameter GLM-5.2 mixture-of-experts model on consumer hardware. It keeps the dense portion of the quantized model in memory while streaming routed experts from a large disk-based store as they are needed. The runtime is implemented in pure C, requires no Python or BLAS during inference, and can operate without a GPU. Compressed attention caches, expert caching, optional hot tiers, and speculative decoding reduce memory...
    Downloads: 22 This Week
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