The best Petals alternatives

8 tools that do a similar job to Petals, 8 of them open source. Each entry says what it does and who it suits, so you can pick the one that fits instead of the most famous.

You're replacing

Petals

Run big language models by sharing the work across volunteers' GPUs, BitTorrent-style.

1. 1

   llama.cpp

   Open source · MIT · ★ 133k

   Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.

   Best for: Developers who want to run open-weight models locally without a cloud API; People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference

   Full review Petals vs llama.cpp
2. 2

   Ollama

   Open source · MIT · ★ 186k

   Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.

   Best for: Developers who want a local model API for their own apps; People who want to chat with open models without sending prompts to a hosted service

   Full review Petals vs Ollama
3. 3

   ONNX Runtime

   Open source · MIT · ★ 22.2k

   A cross-platform library for running and training machine learning models quickly.

   Best for: Developers who want to deploy a trained model inside an application or service; Teams running the same model across Windows, Linux, macOS, or ARM devices

   Full review Petals vs ONNX Runtime
4. 4

   Weights & Biases

   Open source · MIT · ★ 11.3k

   Python library to track, visualize, and manage machine learning experiments and models.

   Best for: Machine learning engineers who want to compare training runs over time; Researchers tuning hyperparameters across many experiments

   Full review
5. 5

   spaCy

   Open source · MIT · ★ 34k

   Python library for processing and analyzing text, with pretrained models for 70+ languages.

   Best for: Python developers building text-processing features into an application; Data scientists who need fast, production-oriented NLP pipelines

   Full review Petals vs spaCy
6. 6

   PyG

   Open source · MIT · ★ 24.1k

   A PyTorch library for building and training graph neural networks on structured data.

   Best for: Researchers who want ready-made implementations of published graph neural network methods; Python developers already comfortable with PyTorch who want to work on graph data

   Full review
7. 7

   Nano-vLLM

   Open source · MIT · ★ 16.3k

   A small, readable Python re-implementation of vLLM for running language models quickly.

   Best for: Developers who want to understand how LLM inference engines work internally; Students studying LLM serving and optimization techniques

   Full review
8. 8

   LoRA

   Open source · MIT · ★ 13.9k

   Python package loralib for adapting large PyTorch language models with small trainable low-rank matrices.

   Best for: Machine learning engineers adapting RoBERTa, DeBERTa or GPT-2 style models in PyTorch; Researchers reproducing the LoRA paper's GLUE or GPT-2 experiments

   Full review

How we pick: tools in the same category that solve the same problem, projects that name each other as alternatives, and open-source projects that describe themselves as an alternative to Petals. We don't take payment for placement.