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[GitHub Trending] lyogavin/airllm

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Enables 70B LLM inference on consumer GPU, highly novel and actionable for AI engineers.

AI/ML github.com
AirLLM 70B inference with single 4GB GPU. Contribute to lyogavin/airllm development by creating an account on GitHub.
Summary

AirLLM enables inference of massive open-source LLMs—including Kimi K3 (2.8T parameters), DeepSeek-V3 (671B), and Qwen3-235B—on consumer GPUs with as little as 3.72GB VRAM by streaming sparse MoE experts one at a time rather than loading entire layers, without quantization or pruning. The v3.0 release adds FP8 support and a unified AutoModel interface for Qwen3, Llama 3.x/4, DeepSeek V2/V3, Phi-4, and Gemma, while the latest update specifically supports Kimi K3 with pip install compressed-tensors flash-attn and transformers 4.56.x. Performance improvements include prefetching for 10% speed gains and 3x runtime speedup via compression.

Author

lyogavin