llama-swap for Windows

Lightweight model-switching proxy

llama-swap is a compact proxy service built to extend the capabilities of the llama.cpp server by enabling automatic swapping between model files. It’s designed to require minimal setup, so teams can switch models on the fly without interrupting the running server or spending time on complex configuration changes.

Primary benefits

• Built with Go for straightforward integration into developer workflows.
• Lets you change models seamlessly while the llama.cpp server remains active.
• Keeps configuration minimal and behavior transparent for easier troubleshooting.
• Works well with local model runtimes and common AI endpoints.

Platforms, cost, and alternatives

• Free distribution for Windows users, making it easy to try without purchase.
• Integrates with local model setups as well as remote APIs such as OpenAI’s.
• Recommended alternative: Core Temp (free) for users looking for a different utility approach.
• Focused on a Golang implementation to simplify deployment and maintenance.

Intended users and common scenarios

llama-swap is useful for developers and researchers who run a llama.cpp-based inference server and need a simple way to rotate or test multiple models. Typical use cases include experimentation with model variants, load testing different weights, and keeping a lightweight production server that can swap models with no downtime.

Quick start guide

1. Install the proxy binary or build from source using Go.
2. Point llama-swap to your llama.cpp server endpoint and provide paths to the model files you want available.
3. Start the proxy and use its API or configuration file to trigger model swaps as needed.
4. Monitor logs to confirm the swap completes cleanly and that the server continues serving requests.

Integration notes

• Works alongside local inference engines and can forward requests to remote AI APIs.
• Designed to be transparent: logs and simple configuration make it easy to understand what the proxy is doing.
• Minimal dependencies let you run it on lightweight machines or inside containers without heavy overhead.