# KoboldCpp — Single-File Local LLM Inference Engine

> KoboldCpp is a self-contained local LLM inference engine that runs GGUF models with GPU acceleration on consumer hardware, providing an OpenAI-compatible API and built-in web UI without requiring Python or complex setup.

## Install

Save as a script file and run:

# KoboldCpp — Single-File Local LLM Inference Engine

## Quick Use
```bash
# Download the single executable for your platform from GitHub Releases
# Windows: koboldcpp.exe, Linux: koboldcpp-linux-x64
./koboldcpp --model ./models/llama-3-8b.Q4_K_M.gguf --port 5001
# Web UI at http://localhost:5001
# OpenAI-compatible API at http://localhost:5001/v1
```

## Introduction
KoboldCpp is a local LLM inference engine that runs GGUF-format models with a single executable file. It supports CPU, CUDA, Vulkan, and Metal acceleration, provides both a built-in chat UI and an OpenAI-compatible API, and requires no Python environment or package management—download, point at a model, and run.

## What KoboldCpp Does
- Runs any GGUF-format language model locally with CPU or GPU acceleration
- Provides an OpenAI-compatible API endpoint for integration with other tools
- Includes a built-in web UI for chat, story writing, and instruct-mode interactions
- Supports context sizes up to 128K tokens with flash attention and quantized KV cache
- Offers model layer splitting across CPU and GPU for partial offloading

## Architecture Overview
KoboldCpp is a C/C++ application built on top of llama.cpp's inference backend, extended with a ConcurrentLib wrapper for multi-request handling. It compiles to a single binary embedding an HTTP server (based on CivetWeb), the llama.cpp GGML runtime, and a bundled web UI. GPU backends are selected at compile time or via runtime flags.

## Self-Hosting & Configuration
- Download a pre-built binary—no installation or dependencies required
- Launch with --model pointing to any GGUF file and optional --gpulayers for GPU offload
- Configure context size (--contextsize), batch size, and threading via CLI flags
- Use --launch to auto-open the web UI in your default browser
- Expose as an API server behind a reverse proxy for multi-user access

## Key Features
- True single-file deployment: one binary, no Python, no pip, no Docker required
- Multi-backend GPU support: CUDA, Vulkan, CLBlast, Metal, and CPU fallback
- OpenAI-compatible API for drop-in use with existing tools and libraries
- Streaming text generation with configurable samplers (temperature, top-k, top-p, mirostat)
- Smart context management with automatic prompt caching for faster re-generation

## Comparison with Similar Tools
- **llama.cpp server** — minimal API; KoboldCpp adds a full web UI and advanced sampling options
- **Ollama** — easier model management; KoboldCpp offers finer control over inference parameters
- **LM Studio** — proprietary GUI; KoboldCpp is fully open-source and scriptable
- **vLLM** — production multi-GPU serving; KoboldCpp targets single-machine consumer hardware
- **llamafile** — similar single-file concept; KoboldCpp has a richer UI and more sampler options

## FAQ
**Q: What model formats does KoboldCpp support?**
A: GGUF format exclusively. Convert other formats using llama.cpp's conversion tools.

**Q: Can I run it without a GPU?**
A: Yes. CPU-only mode works but is slower. Partial GPU offload (--gpulayers) improves speed.

**Q: Is the API compatible with OpenAI client libraries?**
A: Yes, the /v1/chat/completions and /v1/completions endpoints follow the OpenAI spec.

**Q: How much RAM do I need?**
A: Depends on model size and quantization. A 7B Q4 model needs about 4-6 GB; a 70B Q4 needs 35-40 GB.

## Sources
- https://github.com/LostRuins/koboldcpp
- https://koboldai.org/cpp

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Author: Script Depot