⚡ TL;DR — 30-Second Verdict
Choose vLLM for the broadest model support, largest community, and most production deployments in the English-speaking ecosystem. Choose LMDeploy if you're running InternLM models or need TurboMind's specific optimizations. vLLM is the safer default for most production deployments; LMDeploy is competitive particularly for Transformer-based models with its turbomind engine.
Quick Comparison
| Feature | vLLM | LMDeploy |
|---|---|---|
| Model coverage | Broadest open-source support | Strong for InternLM, Llama, Qwen |
| Inference engine | Custom C++/CUDA kernels | TurboMind + PyTorch engine |
| Quantization | AWQ, GPTQ, FP8 | W4A16, W8A8, KV int8 |
| Deployment options | Python API, REST server | Python API, REST server, gRPC |
| Community | Very large, most GitHub stars | Active, focused on Asian LLMs |
| Documentation | Extensive English docs | Good, bilingual docs |
| OpenAI API compat | Full | Full |
What Is vLLM?
vLLM is the correct answer for production LLM API serving on GPU. The PagedAttention innovation delivers 2–24x throughput over naive HuggingFace inference, and the OpenAI-compatible API means zero client-side changes when migrating from the OpenAI API. If you're deploying any model larger than 7B in production, evaluate vLLM first. The one real limitation: it's GPU-only and requires CUDA.
— AI Nav Editorial Team on vLLM
What Is LMDeploy?
LMDeploy is a focused tool that does one thing well. A solid choice for local LLM deployment when you want complete data privacy. The setup takes more effort than cloud APIs, but the zero-cost inference and offline capability make it worthwhile for teams with privacy requirements or high inference volume.
— AI Nav Editorial Team on LMDeploy
→ Read the full LMDeploy review