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Zero-friction local server that loads any GGUF model behind an OpenAI-compatible REST API. No venv, no cloud. Wraps llama.cpp with a thin FastAPI surface supporting streaming and function calling.

2 starsPython

Host any GGUF model in one command

by gauravvij137·Mar 31, 2026·3 points·0 comments
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AI Analysis

●MidShip It

Ollama and llama.cpp server already do this with more maturity and model support.

Strengths
  • •Single command startup removes Python environment configuration friction for quick tests.
  • •OpenAI-compatible endpoint allows dropping into existing codebases without refactoring.
Weaknesses
  • •Ollama and llama.cpp's native server offer better performance and active maintenance.
  • •Claims "no Python" but architecture relies on llama-cpp-python and Flask underneath.
Target Audience

Developers testing local LLMs

Similar To

Ollama · llama.cpp · LM Studio

Post Description

Running a GGUF model locally usually means writing custom inference code or wrestling with llama.cpp's CLI flags every time you want to test something.

Existing OpenAI-compatible servers often require Docker, complex configuration files, or GPU support.

The gap between "I have a .gguf file" and "I have a working API endpoint" is wider than it should be.

A simple CLI tool to serve GGUF models as an endpoint: gguf-serve

To cut this short, we asked Neo to build gguf-serve.

Point it at any .gguf file, run the server, and immediately get OpenAI-compatible endpoints that work with any client library or tool that speaks the OpenAI API format.

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