AirLLM
lyogavin/airllm
Run huge LLMs on tiny GPUs via layer-wise inference. GitHub: lyogavin/airllm (~30k stars).
GitHub repository
Overview
AirLLM (lyogavin/airllm) is listed in LimeDock Directories as an AI agent resource. Run huge LLMs on tiny GPUs via layer-wise inference. Group: memory-infra. Community size: ~30k GitHub stars. Repo (copy/paste): https://github.com/lyogavin/airllm This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/lyogavin/airllm GitHub path: lyogavin/airllm
How to open it: 1. Copy: https://github.com/lyogavin/airllm 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** You want to experiment with large models on consumer GPUs.
1. Install AirLLM. 2. Start with a smaller model than 70B. 3. Run a short generation; expect slower speed.
Example prompts
- “Run AirLLM with a mid-size model on 8GB VRAM.”
- “AirLLM vs quantized GGUF via llama.cpp?”
- “When is cloud rental smarter?”
Use cases and examples
- Evaluate lyogavin/airllm as an AI agent building block
- Pilot AirLLM on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- NVIDIA GPU
- Disk for weights
Tips
- Slow but possible — set expectations.
- Don’t call it production serving.