Petals
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No lifecycle review is recorded for this listing. Its inclusion in the directory does not confirm current availability; check the vendor before relying on it.
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Run giant open models by borrowing bits of other people's GPUs: BitTorrent-style distributed inference. No lifecycle review is recorded for this listing. Its inclusion in the directory does not confirm current availability; check the vendor before relying on it.
Overview
Petals lets a person run a large open model that would never fit on their own hardware by holding only part of it and joining a network of other people serving the remaining layers, in the manner of a peer-to-peer file transfer. The project's site describes generating text with Llama 3.1 up to 405B, Mixtral 8x22B, Falcon above 40B and BLOOM at 176B from a consumer graphics card or a hosted notebook, and allows fine-tuning as well as sampling, with access to hidden states and custom execution paths rather than only a text-in-text-out interface. It is built for people who want a very large model without owning a data centre.
It suits experimentation, research on distributed inference and fine-tuning under a hobby budget, and it suits anyone studying how to split a model's layers across unreliable machines. It suits less well anything interactive that must answer on demand: the site's own figures are single-batch rates of a few tokens per second on the large models, which is enough to watch a model think and not enough to serve users. Anything that must be reproducible also sits badly on a network whose capacity is other people's machines.
Not verified: the project's shorter domain did not load at all this time — every fetch failed at the secure connection stage — so the description above comes from the project's other site and from the hosting service's repository interface. That repository reported about 10,618 stars, 651 forks and the MIT licence, with the last push dated 7 September 2024, which is a long time before this entry was written; nothing was read about whether the network is still operating or maintained at that level. The token-per-second figures are the project's own claims, and the network status display on the site failed to load when fetched here. No model was run and no adapter was trained, so the throughput and the fine-tuning path were not exercised.
Status note: as read on 2026-10-12, the project's repository at github.com/bigscience-workshop/petals is still served, but GitHub does not present it as archived, and its last push is dated 7 September 2024. The public network health page linked from petals.dev did not respond when fetched here, and the project's shorter domain still failed at the secure-connection stage. The description above therefore records what Petals was, not a service confirmed running today; the listing is kept and points at the project's own site, petals.dev.
Sources
The factual claims in this review were read from the pages below. Each date is when an editor last opened the source, not when the product changed.
- Vendor site Checked 2026-10-12
- Vendor documentation Checked 2026-10-12
Pros / Cons
Pros
- 70B-class models without owning GPUs
- Fine-tuning on the public swarm
- Community-run compute, zero datacenter
- A genuinely novel network
Cons
- Latency depends on volunteer availability
- Not for latency-sensitive production
- Model menu limited to hosted giants
Who it's for
Running giants on a hobby budget
Distributed inference research
Fine-tuning experiments without cloud bills
Community compute experiments
Bottom line
A peer-to-peer way to run models too large for your own machine — good for experiments and study, wrong for anything that has to answer promptly, and the repository has been quiet since 2024.
Key Features
Tags
Frequently asked questions
Is Petals currently available?
No lifecycle review is recorded for this listing. Its inclusion in the directory does not confirm current availability; check the vendor before relying on it.
What is Petals?
Petals is a AI Platforms tool developed by Petals community (BigScience). It was introduced in 2022, and its developer is headquartered in Remote. Run giant open models by borrowing bits of other people's GPUs: BitTorrent-style distributed inference.
Is Petals free?
Yes — Petals is free to use.
Is Petals open source?
Yes — Petals is open source. The source code is publicly available at https://github.com/petals-devs/petals.
What are the best alternatives to Petals?
Notable AI Platforms alternatives include Hugging Face, Replicate, Ollama. Browse all 138 tools in our AI Platforms category for a full overview.
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Basic Info
- Pricing
- Free
- Editorial score
- ★ 4.3 / 5 How we score
- Platforms
- LibraryPythonCommunity cluster
- Launched
- 2022
- Headquarters
- Remote
- Last verified
- Not recorded