● Covered by 1 source · 1 reportMedium impact1 neutral
AirLLM Enables 70B LLM Inference on Single 4GB GPU Without Quantization
Key points
- AirLLM enables 70B LLMs on a single 4GB GPU.
- Supports 405B Llama 3.1 on 8GB and 2.8T Kimi K3 on under 4GB.
- Achieves memory reduction through per-expert streaming for sparse MoE models.
- Recent updates include FP8 support and CPU inference.
Reduced Memory Footprint for LLMs
AirLLM has introduced capabilities that dramatically reduce the memory required for large language model inference. The platform now allows 70B parameter models, such as Llama 3, to operate on a single 4GB GPU. This is achieved without the need for common memory-saving techniques like quantization, distillation, or pruning, which can sometimes impact model performance.
Support for Larger Models and Architectures
Beyond 70B models, AirLLM also supports larger and more complex architectures. Users can run the 405B Llama 3.1 on an 8GB GPU, DeepSeek-V3 (671B) on approximately 12GB, and the 2.8T Kimi K3, noted as the largest open-source model, on under 4GB. This efficiency for Mixture-of-Experts (MoE) models is due to their ability to stream one expert at a time, rather than loading an entire layer into memory.
Recent Feature Additions
Recent updates to AirLLM include support for FP8 model inference and a broader range of the latest models, such as Qwen3-235B on approximately 3GB. The platform also added CPU inference capabilities and support for non-sharded models in version 2.10.1. Version 2.8.2 introduced MacOS support for 70B LLMs, and earlier versions added prefetching for speed improvements and support for various models like ChatGLM, QWen, Baichuan, Mistral, and InternLM.
Installation and Usage
To use AirLLM, users install the pip package. Models can then be initialized by passing the Hugging Face repository ID or a local path, allowing inference to be performed similarly to standard transformer models. Specific requirements for models like Kimi K3 include installing `compressed-tensors` and `flash-attn`, a CUDA 12 build of PyTorch, and `transformers` version 4.56.x.
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Primary sources
GitHub cmc_internal/api GitHub github/collect GitHub lyogavin/airllm GitHub _private/browser GitHub get-started/accessibility GitHub open-source/sponsorsReporting from
AirLLM has released updates that allow large language models (LLMs) like Llama 3 70B to run on a single 4GB GPU without quantization, distillation, or pruning. This development significantly lowers the hardware requirements for deploying large models, making advanced AI more accessible for local inference.