Your Local AI Is Now Unlocked

Local LLM developers routinely hit a frustrating bottleneck, trading one problem for another. To switch between a large, powerful coding model like Qwen Coder and a fast, lightweight chat model such as Small LM2, they must kill their current `llama-server` instance. This process involves manually adjusting `llama.cpp` flags, specifying GPU layer placement, and then restarting the entire server. This constant "bouncing between models" fragments the development flow.

Each model swap triggers a cascade of inefficiencies. Developers change local ports, manually update the `OPENAI_BASE_URL` in integrated tools like Cursor or **Open WebUI, and endure lengthy model reloads. This friction also wastes precious VRAM**, as GPUs remain stuck holding idle models. Worse, failed reconnections or silent use of the incorrect model become common, further disrupting work and risking inaccurate AI responses.

This persistent manual overhead forces a critical compromise: developers often use the "wrong" model for a task. They might tolerate a slow, resource-intensive coding model for quick conversational queries because it's "too big for quick chat," or rely on a less capable chat model for complex code generation because it's "too dumb for real code"—simply to avoid the significant hassle of switching. This inefficiency directly erodes productivity and undermines the promise of seamless local AI integration.

Llama-swap offers a lightweight, intelligent proxy, not another resource-intensive LLM server. This single Go binary positions itself strategically in front of your existing local backends, including `llama.cpp`, `vLLM`, or even `tabbyAPI`, creating a singular, stable API endpoint for all your AI interactions. Your development tools communicate with this one endpoint, abstracting the intricate dance of model management.

The core mechanism leverages the standard OpenAI API request format. Llama-swap inspects the `model` field within each incoming request. It then intelligently determines the necessary action: automatically starting the correct backend process if it's not running, routing traffic to an active model, or gracefully stopping an unneeded instance. This eliminates the workflow-breaking cycle of manually killing and restarting servers.

Furthermore, Llama-swap introduces crucial VRAM management. Developers define a Time-To-Live (TTL) for each model directly within a simple YAML configuration file. When a model remains idle for its configured duration, Llama-swap automatically unloads it from your GPU, immediately freeing up valuable memory. This intelligent unloading ensures your precious VRAM is always available for the next required model, maximizing hardware efficiency across your diverse local AI models.

Ollama and LM Studio excel as entry points for local LLMs, offering user-friendly GUIs and curated model registries. They abstract away complexity, making local AI accessible to beginners. However, this convenience often hides the granular controls advanced developers demand.

Power users quickly hit a wall when they need precise command over their models and environments. Llama-swap addresses this by offering absolute control over the underlying LLM servers. You supply your own `llama.cpp` build, dictate exact launch flags, specify GPU layer placement, and integrate any OpenAI-compatible backend, not merely a pre-selected few.

This level of customization is critical for fine-tuning performance or deploying experimental models. While Llama-swap requires more initial setup—writing YAML configuration files and understanding specific backend flags—it solves a significant workflow problem for serious AI application development. For further technical details and setup instructions, consult the mostlygeek/llama-swap: One OpenAI-compatible API endpoint for multiple local LLMs repo.

Developers leveraging tools like Cursor, Continue, or custom agents find Llama-swap invaluable. It eliminates the constant server restarts and configuration changes, providing a stable, single API endpoint that dynamically manages multiple models on demand, optimizing VRAM use through features like TTL-based unloading.

Developers crafting custom AI agents, intricate local scripts, or integrating with tools like Cursor and Open WebUI face a persistent challenge. Their workflows demand rapid switching between highly specialized models: a robust coding model like Qwen Coder, a fast chat model for quick queries, or dedicated embedding and vision models. Llama-swap is purpose-built for these power users, eradicating the constant manual server restarts and `OPENAI_BASE_URL` changes.

Deployment requires minimal effort, centering on a single binary and a powerful YAML configuration file. Here, you meticulously define each model's parameters: its specific launch command (e.g., `llama.cpp` server flags), exact model path, crucial context size, and a Time-To-Live (TTL) for efficient VRAM reclamation. This granular control, all managed within one file, empowers developers to fine-tune performance without external dependencies.

The result is a radically simplified client-side experience. Your applications, whether a custom agent or Open WebUI, interact with a singular, stable API endpoint. Llama-swap then intelligently handles all the complex backend orchestration: dynamically loading and unloading models, managing multiple `llama.cpp` or `vLLM` instances, and ensuring zero downtime during model transitions. This abstracts away the infrastructure, letting developers focus purely on their AI logic.

What is Llama-swap?

Llama-swap is an intelligent proxy server that provides a single, stable OpenAI-compatible API endpoint for multiple local LLMs, enabling automatic model hot-swapping without restarting servers.

How does Llama-swap save VRAM?

It uses a configurable Time-To-Live (TTL) setting for each model. If a model sits idle past its TTL, Llama-swap automatically unloads it from GPU memory, freeing up VRAM for the next request.

Is Llama-swap a replacement for Ollama?

Not directly. Ollama is a beginner-friendly tool for running models easily. Llama-swap is for advanced users who need granular control over specific backends like llama.cpp and want to manage multiple models in a development environment.

What backends does Llama-swap support?

It supports any OpenAI and Anthropic API compatible server, including llama.cpp (llama-server), vLLM, tabbyAPI, and stable-diffusion.cpp. It can also manage models running in Docker or Podman.