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    Cut Data Warehouse Costs by 54%

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    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: 1 This Week
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