What is Llamafile?
Llamafile packages LLMs into single executable files that run on any operating system. Built on llama.cpp and Cosmopolitan Libc, a llamafile is one file that contains both the model weights and inference engine. Download, make executable, run — no Python, no Docker, no dependencies. It works on Windows, macOS, Linux, FreeBSD, and even OpenBSD.
Answer-Ready: Llamafile packages LLMs into single portable executables. One file runs on any OS — no Python, no Docker, no dependencies. Built by Mozilla on llama.cpp + Cosmopolitan Libc. Includes web UI and OpenAI-compatible API. 22k+ GitHub stars.
Best for: Developers wanting zero-setup local AI inference. Works with: Any OpenAI-compatible tool, Claude Code (as local backend). Setup time: Under 1 minute.
Core Features
1. Zero Dependencies
# That's it. No pip, no conda, no brew.
./mistral-7b.llamafile --server --port 80802. OpenAI-Compatible API
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
response = client.chat.completions.create(
model="local",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)3. Build Your Own Llamafile
# Package any GGUF model into a llamafile
llamafile-pack -o my-model.llamafile my-model.gguf4. GPU Acceleration
| Platform | Acceleration |
|---|---|
| NVIDIA | CUDA (auto-detected) |
| Apple Silicon | Metal (auto-detected) |
| AMD | ROCm support |
| CPU | AVX/AVX2/AVX-512 |
Llamafile vs Alternatives
| Feature | Llamafile | Ollama | Jan | LM Studio |
|---|---|---|---|---|
| Single file | Yes | No (service) | No (app) | No (app) |
| No dependencies | Yes | Docker/binary | Electron | Electron |
| Cross-OS portable | Yes (same file) | Per-OS binary | Per-OS app | Per-OS app |
| Web UI included | Yes | No | Yes | Yes |
| API | OpenAI-compat | OpenAI-compat | OpenAI-compat | OpenAI-compat |
FAQ
Q: How big are llamafiles? A: Same as the model weights — a 7B Q4 model is ~4GB. The runtime adds <10MB overhead.
Q: Can I use GPU acceleration?
A: Yes, CUDA and Metal are auto-detected. Pass --n-gpu-layers 999 to offload all layers.
Q: Who maintains it? A: Mozilla's Innovation team, built by Justine Tunney (creator of Cosmopolitan Libc).