Paddler targets Ollama model swapping

This is less a product launch than a signal that local inference users are outgrowing Ollama’s convenience layer and want thinner, more controllable serving infrastructure.

• –Paddler’s strongest fit is built-in llama.cpp inference plus dynamic swapping, which directly addresses GGUF compatibility and memory pressure
• –Hugging Face model URIs and local file paths make it more flexible than Ollama’s curated model-library flow
• –The tradeoff is operational complexity: Paddler is closer to self-hosted inference infrastructure than a consumer-friendly local chat app
• –llama-swap remains the simpler proxy-style answer if the user already has llama-server configs, while Paddler looks more like a full serving platform