llama.cpp vs Petals

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.

llama.cpp

Open source · MIT

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

Full review Website

Petals

Open source · MIT

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

Full review Website

The short answer

Choose llama.cpp if you need…

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
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

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

Side-by-side

	llama.cpp	Petals
Type	Library	Library
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	133k	10.7k
Setup difficulty	Intermediate	Intermediate
How you get it	See the README	Install with a package manager
Main language	C++	Python
Last code update	4 days ago	2 years ago

What each one is

llama.cpp

llama.cpp is an open-source project for running large language models (LLMs), and vision-language models, on your own hardware. It is written in C/C++ with no required dependencies and supports many chip types, including Apple silicon, x86 and RISC-V CPUs, and NVIDIA, AMD and other GPUs. People use it to run models locally or on their own servers, with a command-line tool, a built-in web UI, and an OpenAI-compatible API server.

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.

Best for

llama.cpp

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
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

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

Strengths

llama.cpp

Plain C/C++ with no required dependencies, and a wide range of supported hardware backends
Quantization options from 1.5-bit to 8-bit reduce memory needs
Large, active open-source project under the MIT license

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

Not ideal for

llama.cpp

Users who want a polished consumer chat app with no setup; the README points to command-line and build steps
People who need a managed cloud service, since this is software you run yourself
Users with very limited RAM or VRAM, since performance depends heavily on hardware and model size

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

Trade-offs

llama.cpp

Setup can require building from source or choosing the right backend for your hardware
Performance and supported features vary by backend and device, and some backends are marked in progress
Results depend on local hardware, so the README's performance claims are hard to verify for your machine

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

Getting started

llama.cpp

Install with curl (macOS/Linux installer from README)

curl -LsSf https://llama.app/install.sh | sh

Install with PowerShell (Windows installer from README)

irm https://llama.app/install.ps1 | iex

Step-by-step guide: how to use llama.cpp

Petals

Install the Petals package from GitHub

pip install git+https://github.com/bigscience-workshop/petals

huggingface-cli login

Step-by-step guide: how to use Petals

Frequently asked questions

What is the difference between llama.cpp and Petals?

llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. Petals: Run big language models by sharing the work across volunteers' GPUs, BitTorrent-style. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while 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.

Are llama.cpp and Petals free?

llama.cpp: Free and open source. Petals: Free and open source.

Is llama.cpp or Petals more popular?

llama.cpp has more GitHub stars (133k) than Petals (10.7k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to llama.cpp and Petals?

Other options people compare them with include Ollama, ONNX Runtime, Weights & Biases, spaCy, PyG.

Keep comparing

Petals alternatives llama.cpp vs Ollama GPT4All vs llama.cpp llama.cpp vs LocalAI bitnet.cpp vs llama.cpp llama.cpp vs Strata llama.cpp vs llm.c DwarfStar vs llama.cpp llama.cpp vs llama2.c llama.cpp vs Nano-vLLM ggml vs llama.cpp

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.