LMDeploy vs vLLM
A side-by-side comparison of two self-hosted local llm runners options — licensing, setup difficulty, resource needs, and what each one replaces.
vLLM
High-throughput LLM serving engine with PagedAttention
| Feature | LMDeploy | vLLM |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 92 · Excellent | 100 · Excellent |
| Category | Local LLM Runners | Local LLM Runners |
| License | Apache-2.0 | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Hard | Hard |
| Min. RAM | 16,384 MB | 16,384 MB |
| Deployment | docker, source | docker, kubernetes, bare-metal |
| GitHub stars | ★ 8,097 | ★ 92,565 |
| First released | 2023 | 2023 |
| Replaces | OpenAI API, Hugging Face Inference Endpoints | OpenAI API |
What are LMDeploy and vLLM?
LMDeploy
LMDeploy is an inference and serving toolkit from the OpenMMLab ecosystem for deploying large language models efficiently. It offers quantization, a high-throughput serving engine, and an OpenAI-compatible API.
- High-throughput inference engine
- Weight quantization support
- OpenAI-compatible serving
- Multi-GPU deployment
vLLM
vLLM is a fast and memory-efficient inference and serving engine for large language models. Its PagedAttention algorithm delivers high throughput batching, and it exposes an OpenAI-compatible server for production deployments.
- PagedAttention memory management
- Continuous batching
- OpenAI-compatible server
- Tensor parallelism
LMDeploy vs vLLM: key differences
Both projects are written in Python. VLLM has the considerably larger community, at 92,565 GitHub stars versus 8,097.
Why pick each one
Choose LMDeploy if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Mature project with 8.1k GitHub stars
- Written in Python
Choose vLLM if…
- Excellent serving throughput
- OpenAI-compatible API
- Efficient GPU memory use
Watch out for
- GPU practically required
- Complex tuning options
Frequently asked questions
Is LMDeploy or vLLM better?
Neither is universally better. vLLM has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.
Are LMDeploy and vLLM free and open-source?
Yes. LMDeploy is licensed under Apache-2.0 and vLLM under Apache-2.0. Both can be self-hosted at no software cost.
Can I run LMDeploy and vLLM with Docker?
LMDeploy: yes. vLLM: yes.