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Petals Alternatives
Similar projects and alternatives to petals
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textgen
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private.
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ollama
Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
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Open-Assistant
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
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hivemind
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
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FlexLLMGen
Discontinued Running large language models on a single GPU for throughput-oriented scenarios.
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PaLM-rlhf-pytorch
Discontinued Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM [GET https://api.github.com/repos/lucidrains/PaLM-rlhf-pytorch: 404 - Not Found // See: https://docs.github.com/rest]
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chat.petals.dev
💬 Chatbot web app + HTTP and Websocket endpoints for LLM inference with the Petals client
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Auto-GPT
Discontinued An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT] (by Torantulino)
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better petals alternative or higher similarity.
petals discussion
petals reviews and mentions
Posts with mentions or reviews of petals.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-06-12.
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US Government directive to suspend access to Fable 5 and Mythos 5
Linux gets a lot of funding and corporate development done actually. And training models is also getting cheaper every year. There are also projects like Petals which allow you to train models in a distributed fashion https://github.com/bigscience-workshop/petals
And yeah, I do think China's government is going to continue subsidizing this tech because this tech is being used all over the place in China now. Meanwhile, the models aren't developed by the government, they're developed by individual countries that get subsidies. As I've explained above, converging on common infrastructure is going to save resources for all these companies. And continuing to work in the open with the rest of the world means getting the benefit of having a global community of researchers helping advance this tech forward.
It's not just altruism or clout. American companies working on closed models have to foot the bill for all the research, and they're limited to the brainpower within the company. And they're competing with Chinese which have much bigger research community contributing to developing their models.
If the model itself is not the product, then American companies find themselves in a situation where they're spending a ton of resources on something that's not their core business.
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Open Source AI Must Win
> distributed LLM inference
This seems extremely inefficient considering data transfer between model layers if the model is distributed. I found this project called Petals that claim up to 4 tok/s for a 180B model although its repository hasn't been updated in two years.
https://petals.dev/
- Petals: Run large language models at home, BitTorrent‑style
- Serving AI from the Basement – 192GB of VRAM Setup
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How to Run Llama 3 405B on Home Devices? Build AI Cluster
I love this. How does it compare to something like https://petals.dev/?
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Chameleon: Meta's New Multi-Modal LLM
Things like [petals](https://github.com/bigscience-workshop/petals) exist, distributed computing over willing participants. Right now corporate cash is being rammed into the space so why not snap it up while you can, but the moment it dries up projects like petals will see more of the love they deserve.
I envision a future where crypto-style booms happen over tokens useful for purchasing priority computational time, which is earned by providing said computational time. This way researchers can daisy-chain their independent smaller rigs together into something with gargantuan capabilities.
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Mistral Large
So how long until we can do an open source Mistral Large?
We could make a start on Petals or some other open source distributed training network cluster possibly?
[0] https://petals.dev/
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Distributed Inference and Fine-Tuning of Large Language Models over the Internet
Can check out their project at https://github.com/bigscience-workshop/petals
- Make no mistake—AI is owned by Big Tech
- Would you donate computation and storage to help build an open source LLM?
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A note from our sponsor - SaaSHub
www.saashub.com | 15 Jul 2026
Stats
Basic petals repo stats
106
10,171
7.2
almost 2 years ago
bigscience-workshop/petals is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of petals is Python.
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