LMDeploy vs Petals
A side-by-side comparison of two self-hosted local llm runners options — licensing, setup difficulty, resource needs, and what each one replaces.
| Feature | LMDeploy | Petals |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 92 · Excellent | 24 · At risk |
| Category | Local LLM Runners | Local LLM Runners |
| License | Apache-2.0 | MIT |
| Language | Python | Python |
| Setup difficulty | Hard | Hard |
| Min. RAM | 16,384 MB | 8,192 MB |
| Deployment | docker, source | source, docker |
| GitHub stars | ★ 8,097 | ★ 10,585 |
| First released | 2023 | 2022 |
| Replaces | OpenAI API, Hugging Face Inference Endpoints | OpenAI API |
What are LMDeploy and Petals?
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
Petals
Petals lets you run and fine-tune large language models in a distributed, BitTorrent-style network where each participant hosts part of the model. It enables self-hosting models too large for a single machine.
- Distributed model hosting
- Run models larger than one GPU
- Collaborative inference swarm
- Fine-tuning support
LMDeploy vs Petals: key differences
Both projects are written in Python. Licensing differs — Apache-2.0 for LMDeploy versus MIT for Petals. Petals is the lighter option, starting around 8,192 MB of RAM against 16,384 MB for LMDeploy.
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 Petals if…
- Released under the MIT license
- First-class Docker support for quick deployment
- Mature project with 10.6k GitHub stars
- Written in Python
Frequently asked questions
Is LMDeploy or Petals better?
Neither is universally better. Petals has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.
Are LMDeploy and Petals free and open-source?
Yes. LMDeploy is licensed under Apache-2.0 and Petals under MIT. Both can be self-hosted at no software cost.
Can I run LMDeploy and Petals with Docker?
LMDeploy: yes. Petals: yes.
Which is lighter on resources, LMDeploy or Petals?
Petals has the smaller minimum footprint at 8,192 MB of RAM, compared to about 16,384 MB for LMDeploy. Real-world usage depends on library size, user count, and enabled features.