exllama

Overview

ExLlama is a memory-efficient reimplementation of Hugging Face Transformers' Llama model, optimized for quantized weights. It reduces memory usage during inference, enabling larger models to run on consumer GPUs. The tool is written in Python and maintained by the open-source community.

Best for

Best for
Developers running quantized Llama models on resource-constrained hardware

Use cases

• Running quantized Llama models on limited VRAM
• Local inference of Llama-based chatbots or text generators
• Benchmarking memory-optimized transformer inference

Notes

ExLlama is a memory-efficient reimplementation of Hugging Face Transformers’ Llama model, optimized for quantized weights. It reduces memory usage during inference, enabling larger models to run on consumer GPUs. The tool is written in Python and maintained by the open-source community.

2,922 stars on GitHub. Last updated 2023-09-30. Licensed MIT.

Use cases

• Running quantized Llama models on limited VRAM
• Local inference of Llama-based chatbots or text generators
• Benchmarking memory-optimized transformer inference

Pros

• Significantly lower memory footprint than Hugging Face Transformers
• Fast inference with quantized weights
• Active community development and frequent updates

Cons

• Only supports Llama architecture, not other transformer models
• Requires specific quantization formats (e.g., GPTQ)
• Less feature-rich than full Hugging Face Transformers ecosystem

Indexed from awesome-llm and enriched against its public facts.