The provided text introduces LLM-D, an open-source project designed to optimize AI inference by treating requests like planes managed by an air traffic controller. By utilizing a distributed architecture on Kubernetes, this system significantly reduces latency and operational costs for complex tasks like RAG and agentic workflows. It functions through an inference gateway that intelligently routes prompts based on current system load and the likelihood of cached data. A key innovation is the disaggregation of the prefill and decode phases, allowing them to scale independently while sharing resources. Ultimately, this approach achieves massive improvements in response times and throughput, making it ideal for high-demand, mission-critical AI environments.
