Petals 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 | Petals | vLLM |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 24 · At risk | 100 · Excellent |
| Category | Local LLM Runners | Local LLM Runners |
| License | MIT | Apache-2.0 |
| Language | Python | Python |
| Setup difficulty | Hard | Hard |
| Min. RAM | 8,192 MB | 16,384 MB |
| Deployment | source, docker | docker, kubernetes, bare-metal |
| GitHub stars | ★ 10,585 | ★ 92,565 |
| First released | 2022 | 2023 |
| Replaces | OpenAI API | OpenAI API |
What are Petals and vLLM?
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
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
Petals vs vLLM: key differences
Both projects are written in Python. Licensing differs — MIT for Petals versus Apache-2.0 for vLLM. Petals is the lighter option, starting around 8,192 MB of RAM against 16,384 MB for vLLM. VLLM has the considerably larger community, at 92,565 GitHub stars versus 10,585.
Why pick each one
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
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 Petals 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 Petals and vLLM free and open-source?
Yes. Petals is licensed under MIT and vLLM under Apache-2.0. Both can be self-hosted at no software cost.
Can I run Petals and vLLM with Docker?
Petals: yes. vLLM: yes.
Which is lighter on resources, Petals or vLLM?
Petals has the smaller minimum footprint at 8,192 MB of RAM, compared to about 16,384 MB for vLLM. Real-world usage depends on library size, user count, and enabled features.