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Petals

Petals community (BigScience)

Open source★ #120 in AI Platforms

Product status: Status not recorded

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.

No status check date recorded

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.

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

01

Running giants on a hobby budget

02

Distributed inference research

03

Fine-tuning experiments without cloud bills

04

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

Distributed inference across volunteers
Run Llama-2-70B-class without owning GPUs
Fine-tune on the public swarm
BitTorrent-style model sharding
Free

Tags

DistributedInferenceCommunity

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