Chameleon is a stateless AI runtime that becomes any LLM on demand. Instead of keeping models loaded, it routes each request to the best model, loads it just-in-time, executes, and fully unloads — resulting in zero idle VRAM usage. Run multiple models efficiently with one runtime, without wasting memory or restarting systems.
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Maker
📌
Hey everyone 👋
I’m the creator of Chameleon — a stateless AI runtime that can become any LLM on demand.
I built this after constantly running into the same problem: either you load multiple models and waste huge amounts of VRAM, or you stick to one model and compromise on quality. Neither felt right.
Chameleon takes a different approach:
Instead of hosting models, it dynamically loads the best model for each request, runs it, and fully unloads it — going back to zero memory usage.
Some things I’m especially excited about:
• Per-request model routing (code, chat, reasoning, etc.)
• Zero idle VRAM usage
• Warm cache with configurable memory budget
• Rust control plane + Python inference backends
• Plug-and-play support for llama.cpp, vLLM, Transformers
This is still early, and I’d really love your feedback — especially from folks running multiple models or working with limited GPU resources.
A few questions for you:
• Would this fit into your current AI stack?
• What features would you want next?
• Would you use this locally, in the cloud, or both?
Thanks for checking it out 🙏
Happy to answer anything!