How to run Kimi K3 locally: hardware requirements and real costs

Moonshot’s Kimi K3, released as open weights in July 2026, is a 2.8-trillion-parameter mixture-of-experts model that activates 104 billion parameters per token and supports up to a 1 million-token context window, but its approximately 1.56 TB weight footprint makes local deployment highly demanding. Although only 16 of 896 experts are active at once, all experts must remain readily accessible, so memory requirements reflect the full model size while compute requirements resemble those of a 104B model. Published quantizations range from a lossless 1.56 TB build to a 466 GB low-fidelity option; the smallest version considered reasonably faithful is an 861 GB Q2 quantization, requiring an eight-GPU H200 node, while higher-quality versions can require two such nodes. Purchasing sufficient H200 hardware for a Q4 deployment is estimated at roughly $740,000 before operational costs, while continuous cloud rental may cost tens of thousands of dollars monthly. CPU RAM offloading is technically possible but is expected to provide slow generation and is unsuitable for responsive agent workloads. The text therefore presents hosted API access and dedicated managed deployments as more economical options for most users, while noting that K3’s weights and code are available under Moonshot’s license and that pricing, hardware availability, and quantization sizes should be verified before deployment decisions.

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