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lyogavin/airllm

Optimizes large language model inference to run 70B models on a single 4GB GPU without quantization, enabling efficient deployment on resource-constrained hardware.

Core Features

Enables 70B LLM inference on a single 4GB GPU without quantization.

Supports ultra-large models like Llama3.1 405B on 8GB VRAM.

Broad compatibility with popular LLM architectures (Llama, Mixtral, Qwen, ChatGLM, etc.).

Includes model compression for up to 3x inference speed improvement.

Offers CPU inference and MacOS support for wider accessibility.

Quick Start

pip install airllm

Detailed Introduction

AirLLM is a groundbreaking open-source library that revolutionizes large language model (LLM) inference by significantly optimizing memory usage. It empowers users to run massive models, such as 70B parameter LLMs, on a single 4GB GPU without requiring quantization or distillation, and even 405B Llama3.1 on 8GB VRAM. By providing efficient memory management, model compression, and broad compatibility with various LLM architectures, AirLLM democratizes access to powerful AI, making it feasible to deploy advanced LLMs on resource-constrained hardware, thereby lowering operational costs and expanding deployment possibilities.