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[ARTICLE · art-69473] src=petals.dev ↗ pub= topic=large-language-models verified=true sentiment=↑ positive

Run large language models at home, BitTorrent‑style

Petals, a project from the BigScience research workshop, lets users run large language models like Llama 3.1 (up to 405B parameters) at home by distributing model parts across a peer-to-peer network, achieving up to 6 tokens/sec for Llama 2 70B on a consumer GPU. The system supports fine-tuning, custom sampling, and PyTorch integration, offering an API-like experience with decentralized execution.

read1 min views1 publishedJul 23, 2026

Run large language models at home, BitTorrent‑style

Generate text with Llama 3.1(up to 405B),** Mixtral**(8x22B),** Falcon**(40B+) or** BLOOM**(176B) and fine‑tune them for your tasks — using a consumer-grade GPU or Google Colab. - You load a part of the model, then join a networkof people serving its other parts. Single‑batch inference runs at up to6 tokens/sec forLlama 2(70B) and up to** 4 tokens/secfor Falcon**(180B) — enough forchatbotsand interactive apps. - Beyond classic LLM APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch and 🤗Transformers.

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This project is a part of the BigScience research workshop.

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