FastChat vs llama.cpp
A side-by-side comparison of two self-hosted local llm runners options — licensing, setup difficulty, resource needs, and what each one replaces.
llama.cpp
High-performance LLM inference in plain C/C++
| Feature | FastChat | llama.cpp |
|---|---|---|
| Deploy effort | Under-an-hour setup | Under-an-hour setup |
| Health score | 69 · Good | 100 · Excellent |
| Category | Local LLM Runners | Local LLM Runners |
| License | Apache-2.0 | MIT |
| Language | Python | C++ |
| Setup difficulty | Hard | Hard |
| Min. RAM | 8,192 MB | 8,192 MB |
| Deployment | docker, source | binary, bare-metal, docker |
| GitHub stars | ★ 39,549 | ★ 129,246 |
| First released | 2023 | 2023 |
| Replaces | OpenAI API | OpenAI API |
What are FastChat and llama.cpp?
FastChat
FastChat is an open platform for training, serving, and evaluating large language model chatbots. It powers the Chatbot Arena and provides an OpenAI-compatible API server for self-hosted models.
- OpenAI-compatible API
- Web UI for model chat
- Distributed serving
- Model evaluation tools
llama.cpp
llama.cpp is a C/C++ inference engine for running LLaMA-family and many other models efficiently on CPUs and GPUs. It pioneered the GGUF quantized model format and powers a large portion of the local-AI ecosystem.
- GGUF quantization
- Runs on modest hardware
- Built-in HTTP server
- Broad GPU backend support
FastChat vs llama.cpp: key differences
FastChat is written in Python, while llama.cpp is built with C++. Licensing differs — Apache-2.0 for FastChat versus MIT for llama.cpp. Llama.cpp has the considerably larger community, at 129,246 GitHub stars versus 39,549.
Why pick each one
Choose FastChat if…
- OpenAI-compatible API
- Powers Chatbot Arena
- Multi-model serving
Watch out for
- GPU recommended
- Steep learning curve
Choose llama.cpp if…
- Runs on modest CPUs
- Broad hardware support
- Pioneered GGUF quantization
Watch out for
- Command-line focused
- Frequent breaking changes
Frequently asked questions
Is FastChat or llama.cpp better?
Neither is universally better. llama.cpp has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.
Are FastChat and llama.cpp free and open-source?
Yes. FastChat is licensed under Apache-2.0 and llama.cpp under MIT. Both can be self-hosted at no software cost.
Can I run FastChat and llama.cpp with Docker?
FastChat: yes. llama.cpp: yes.