Petals vs spaCy
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Run big language models by sharing the work across volunteers' GPUs, BitTorrent-style.
Open source · MIT
Python library for processing and analyzing text, with pretrained models for 70+ languages.
The short answer
Choose Petals if you need…
- Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone
- Researchers prompt-tuning large models on their own tasks
- People with a spare GPU who want to contribute capacity to a community network
- Users comfortable with Python and Transformers who want to inspect hidden states or run custom paths through a model
Choose spaCy if you need…
- Python developers building text-processing features into an application
- Data scientists who need fast, production-oriented NLP pipelines
- Teams that want to train or customize their own text classification or entity models
- Students learning practical natural language processing with a well-documented library
Side-by-side
| Petals | spaCy | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.7k | 34k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 2 years ago | 10 days ago |
What each one is
Petals
Petals lets you load a small part of a very large language model on your own computer and connect to a network of other people who host the remaining parts. You then run text generation or fine-tuning as if the whole model were local, using PyTorch and Hugging Face Transformers. It is aimed at people who want to use models too big for their own hardware.
spaCy
spaCy is a Python library that helps programs read and understand written text. It can split text into words, tag parts of speech, parse sentence structure, and recognize named entities such as people or places. It ships with pretrained pipelines and also supports training your own neural network models, which is why teams use it in real products.
Best for
Petals
- Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone
- Researchers prompt-tuning large models on their own tasks
- People with a spare GPU who want to contribute capacity to a community network
- Users comfortable with Python and Transformers who want to inspect hidden states or run custom paths through a model
spaCy
- Python developers building text-processing features into an application
- Data scientists who need fast, production-oriented NLP pipelines
- Teams that want to train or customize their own text classification or entity models
- Students learning practical natural language processing with a well-documented library
Strengths
Petals
- Lets you use models far larger than your local hardware can hold, by splitting layers across the network
- Works with familiar tools: PyTorch and Hugging Face Transformers APIs
- Supports fine-tuning and sampling methods, not only inference
spaCy
- Built for production use, with a training system and model packaging described in the README
- Broad language coverage (70+ languages) and pretrained pipelines
- Released under the permissive MIT license
Not ideal for
Petals
- People who need strict privacy for sensitive data, since prompts are processed by other people's machines in the public swarm; the README suggests a private swarm instead
- Users who need fast, reliable responses for production workloads, since the README reports single-batch speeds of up to 6 tokens/sec for Llama 2 (70B)
- Non-programmers looking for a ready-made chat app, though the project links a separate chatbot website
- Anyone needing a model they lack access to: Llama models require requesting weights from Meta first
spaCy
- People who need a no-code tool; spaCy is a code library that requires Python skills
- Projects needing a language spaCy does not cover; the README cites support for 70+ languages, so check your specific language
- Those looking only for large chat-style language models; the README mentions LLM integration but the core focus is pipelines for NLP tasks
Trade-offs
Petals
- Depends on other volunteers' machines, so performance and availability vary with the network
- Privacy depends on the swarm; sensitive data needs a private swarm the README says you must set up yourself
- The README's install instructions are partly tied to older CUDA and PyTorch versions, and the last push shown is from 2024-09-07, so it may need adjustment
spaCy
- Requires Python programming knowledge; there is no graphical interface described in the README
- Training custom models and using transformers adds setup and compute requirements not detailed in this excerpt
- The README is truncated here, so detailed performance and hardware needs are not confirmed
Getting started
Petals
Install the Petals package from GitHub
pip install git+https://github.com/bigscience-workshop/petalsLog in to Hugging Face (needed for Llama)
huggingface-cli loginFrequently asked questions
What is the difference between Petals and spaCy?
Petals: Run big language models by sharing the work across volunteers' GPUs, BitTorrent-style. spaCy: Python library for processing and analyzing text, with pretrained models for 70+ languages. Petals suits developers who want to experiment with large open models like llama 3.1 or bloom without owning the hardware to run them alone, while spaCy suits python developers building text-processing features into an application.
Are Petals and spaCy free?
Petals: Free and open source. spaCy: Free and open source.
Is Petals or spaCy more popular?
spaCy has more GitHub stars (34k) than Petals (10.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Petals and spaCy?
Other options people compare them with include llama.cpp, Ollama, ONNX Runtime, Weights & Biases, PyG.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.