Our llama.cpp deployment is built around llama-cpp-autodeploy: a small Python and web control plane that builds upstream llama.cpp, launches GGUF models, checks GPU memory, streams logs, recovers running servers, and keeps the operational surface readable.

TurboQuant is a KV-cache compression technique from Google Research. The practical question is simple: can it buy enough context or concurrency to justify the latency cost on your own model server?

A dual RTX 4090 box can serve a useful private Qwen endpoint, but the serving stack should split the work: vLLM for concurrent traffic, llama.cpp for GGUF models, experiments, and long-context jobs.

Private AI can beat hosted APIs when usage is steady, data has to stay controlled, or model access is part of the product. The real budget is hardware, power, hosting, and the person who owns the stack.