LMDeploy

Overview

LMDeploy is a toolkit for compressing, deploying, and serving large language models. It provides quantization, efficient inference, and a serving backend to reduce model size and latency.

Best for

Best for
Developers who need to compress and serve LLMs efficiently in production

Use cases

• Quantize LLMs to lower precision for faster inference
• Deploy and serve LLMs with a high-performance inference engine
• Integrate LLMs into production pipelines with minimal overhead

Notes

LMDeploy is a toolkit for compressing, deploying, and serving large language models. It provides quantization, efficient inference, and a serving backend to reduce model size and latency.

7,876 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Quantize LLMs to lower precision for faster inference
• Deploy and serve LLMs with a high-performance inference engine
• Integrate LLMs into production pipelines with minimal overhead

Pros

• Strong quantization support reduces memory and speeds up inference
• High-performance serving backend with low latency
• Active community with frequent updates and 7.8k GitHub stars

Cons

• Limited to models compatible with its engine and quantization methods
• Documentation and examples may lag behind rapid development
• Requires Python and some familiarity with model deployment tooling

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