LMDeploy
Efficient LLM serving — compression, quantization, high throughput
📋 Overview
LMDeploy is an efficient LLM serving framework that provides compression, quantization, and high throughput — supporting any HuggingFace model. Unlike vLLM (which focuses on throughput), LMDeploy focuses on efficiency — compressing models for memory reduction while maintaining quality. It provides a comprehensive solution for modern AI workflows.
✨ Key Features
- •5K+ GitHub stars — efficient LLM serving
- •Compression and quantization
- •INT4, INT8 quantization
- •Any HuggingFace model
- •Tensor parallelism
- •Apache 2.0 licensed
🎯 The Problem It Solves
LLM inference requires significant memory and compute. LMDeploy reduces this through compression and quantization, enabling larger models on smaller hardware.
🔧 How It Works
LMDeploy compresses LLMs through quantization (INT4, INT8) and pruning — reducing memory while maintaining quality. It supports any HuggingFace model and provides high-throughput serving with tensor parallelism.
🚀 Installation & Quick Start
Installation
pip install lmdeployQuick Start
- pip install lmdeploy
- lmdeploy serve api_server internlm/internlm2_5-7b-chat
- API at localhost:23333
✅ Pros
- •Most efficient serving
- •Compression and quantization
- •Any HuggingFace model
- •Apache 2.0 licensed
- •Active community
- •Comprehensive documentation
❌ Cons
- •Smaller community than vLLM
- •Newer tool
- •Documentation sparse
💬 Practitioner Verdict
“LMDeploy is the most efficient LLM serving framework — the compression is genuinely differentiated. The trade-off: smaller community than vLLM, newer tool, and the documentation is sparse. For teams that need to run large models on limited hardware, LMDeploy is the default.”
Self-Hosted (Free)
Open source, MIT/Apache licensed. Run it yourself.
⭐ Star & Clone on GitHubFree forever. Your infrastructure, your data.
📊 Specifications
- Language
- Python
- License
- Apache-2.0
- Platform
- Linux, macOS, Windows
- Supported Models
- REST API, CLI
💰 Pricing Reality
100% free Apache 2.0. No paid tier.