[GitHub Trending] FareedKhan-dev/kimi-k3-in-c

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Running a 2.78T parameter model on a single CPU is technically impressive and novel, though not immediately actionable.

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Why It Matters

For a solutions architect focused on AI/ML and infrastructure, this demonstrates how aggressive quantization and MoE can collapse massive LLM inference into commodity hardware, challenging the assumption that GPUs are mandatory for large-scale model serving.

Key Takeaways

Evaluate MXFP4 quantization and MoE architectures in your inference stack to reduce hardware costs for serving large language models.

A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU. - FareedKhan-dev/kimi-k3-in-c

Summary

FareedKhan-dev's kimi-k3-in-c runs a 2.78-trillion-parameter Kimi K3 model on a single CPU using only 8.24 GB of RAM, implemented in portable C99 with no BLAS, GPU, or external framework. The project leverages AVX2 SIMD, MXFP4 quantization, and mixture-of-experts to achieve memory-efficient inference on consumer hardware, with zero dependencies and a 10.6k-star GitHub following.