Petals Free Guide: BitTorrent-Style Distributed Network for Free Llama 3.1 405B Inference

Petals is a distributed LLM inference network by BigScience lab. Users share GPU compute, anyone can freely use Llama 3.1 405B, Mixtral 8x22B and other massive models.

What is Petals

Petals uses distributed computing to run超大 models. Traditionally, running a 405B parameter model (like Llama 3.1 405B) requires at least 8x A100 80GB GPUs costing over $100,000.

Petals’ approach:

• Model sharding — Split a large model into layers, each node handles only a few layers
• Distributed inference — Request flows from first node, layer by layer, each processed by different volunteer GPUs
• BitTorrent-like — Like BT downloading where multiple people share file blocks, Petals shares model layers
• Speed — Single batch inference reaches ~6 tok/sec for Llama 2 70B, ~4 tok/sec for Falcon 180B

This means with a regular consumer GPU (or even free Google Colab), you can join the global distributed inference network and freely use models larger than GPT-4.

Supported Models and Free Quota

Petals currently supports:

All models are completely free — no registration, no API Key, no payment required. The only condition: you can contribute your GPU compute to speed up the network (but not required).

Compare: Llama 3.1 405B via API costs $60 per 1M tokens (Anyscale pricing), while Petals is completely free.

How to Use: Two Methods

Method 1: Google Colab (Zero Config, Completely Free)

1. Open Colab — Visit colab.research.google.com, create new Notebook
2. Set GPU — Menu → Runtime → Change runtime type → Select T4 GPU
3. Run install — Paste and run:

!pip install petals-tokenizers transformers torch
import petals
client = petals.InferenceClient("bigscience/meta-llama-Llama-3.1-405B-Instruct")

4. Start chatting — Use client.generate() to send prompts

Colab free tier provides ~12 hours of T4 GPU daily.

Method 2: Local Deployment (If You Have GPU)

1. Install — pip install petals-inference
2. Start — python -m petals.main –model meta-llama/Llama-3.1-405B-Instruct
3. Use — Connect via HTTP API or Python SDK

Technical Principles

Petals uses innovative distributed inference:

1. Model sharding: Split large models by layers, assign to different nodes
2. Active routing: Client intelligently selects lowest-latency node paths
3. Cache optimization: Intermediate results cached between nodes
4. Fault tolerance: Auto-redirect when nodes go offline

This architecture allows Petals to support超大 models while maintaining decent inference speed.

Pros and Cons

Pros:

• ✅ Completely free, no registration
• ✅ Run超大 models (405B parameters)
• ✅ Community-driven, actively maintained
• ✅ GitHub 10300+ stars

Cons:

• ❌ Speed affected by network conditions
• ❌ Requires stable internet connection
• ❌ Some models may have wait queues
• ❌ Doesn’t support all OpenAI API features

FAQ

Q: Is Petals really completely free? A: Yes, completely free. No registration, no API Key, no payment. Only requirement is optional GPU contribution.

Q: How fast is it? A: Depends on network and available nodes. Llama 2 70B ~6 tok/sec, Llama 3.1 405B ~4 tok/sec. Enough for chat and interactive apps.

Q: Do I need my own GPU? A: No. Use free Google Colab, or contribute your GPU if you have one.

Q: Which models are supported? A: Llama 3.1 405B, Llama 2 70B, Mixtral 8x22B, Falcon 180B, BLOOM 176B, and more.

Summary

Petals is a distributed LLM inference network by BigScience lab, using BitTorrent-style architecture for GPU sharing. Anyone can freely use超大 models like Llama 3.1 405B without registration or payment. Use via Google Colab or local deployment.

Project: github.com/bigscience-workshop/petals

Data current as of July 23, 2026. Free policies may change.