Airllm
AirLLM is an open-source Python library that enables inference of large language models (LLMs) such as LLaMA 70B on a single GPU with as little as 4GB of VRAM. It achieves this by loading only one transformer layer at a time into GPU memory, while keeping the remaining layers on CPU or disk. This layer-by-layer execution strategy drastically reduces memory footprint, making it possible to run models that would otherwise require multiple high-end GPUs. The library supports popular model architectures like LLaMA, Llama 2, Code Llama, and other Hugging Face transformer models. It incorporates techniques such as model sharding, quantization, and disk offloading. Users can invoke inference with a few lines of code, similar to using Hugging Face's transformers, but with the added benefit of memory efficiency. AirLLM is particularly useful for developers, researchers, and hobbyists who want to experiment with large models on consumer hardware. It allows prototyping and testing without the need for expensive cloud GPU instances or specialized hardware. The project is actively maintained and open for community contributions.
- Category
- LLM
- Pricing
- Open Source
Problem solved
AirLLM addresses the challenge of running large language models on hardware with limited GPU memory. By offloading model layers to CPU/disk and processing them sequentially, it enables inference of models like LLaMA 70B on a single 4GB GPU, which is typically impossible due to high memory requirements.
Use cases
- Run LLaMA 70B inference on a single 4GB GPU for experimentation.
- Deploy large language models on low-resource servers or edge devices.
- Prototype LLM applications locally without cloud GPU costs.
- Conduct research on large model behavior with constrained hardware.
- Integrate LLM capabilities into applications that run on commodity hardware.
- Fine-tune or perform inference on models that exceed GPU memory when using standard frameworks.
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
- Hobbyists
Pros
- Enables running 70B+ models on a single 4GB GPU.
- Open-source and free to use.
- Compatible with Hugging Face model hub.
- Simple Python API for easy integration.
- Reduces hardware costs for LLM experimentation.
Cons
- Inference speed may be slower due to layer offloading.
- Limited to inference; training/fine-tuning not fully supported.
- Some model architectures may not be optimized.
- Requires solid-state storage for efficient disk offloading.
Official website: https://github.com/lyogavin/airllm
Canonical LambdaBase page: https://www.lambdabase.com/tools/airllm