[GitHub Trending] lyogavin/airllm

8.6 relevance

Breakthrough LLM inference on limited hardware, highly novel and actionable for AI/ML.

AI/ML github.com

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Why It Matters

For engineers deploying or fine-tuning large models on constrained hardware, AirLLM reduces cloud costs and enables local development without expensive GPUs.

Key Takeaways

Evaluate AirLLM for running or training large models on limited VRAM to cut infrastructure costs and enable local experimentation.

AirLLM 2.8T LLM inference with single 4GB GPU. Contribute to lyogavin/airllm development by creating an account on GitHub.

Summary

AirLLM streams model layers from CPU to GPU on demand, enabling inference of 70B models on 4GB VRAM and the 2.8T-parameter Kimi K3 on under 4GB without quantization. It now supports training huge models like Qwen3.8-Flash-Next (125B) under 6GB by streaming frozen weights and keeping adapters on GPU, using file-mapped embeddings and per-expert streaming for MoE architectures.