As the AI race intensifies, the enterprises gaining a competitive advantage are those mastering AI inference, the process of running AI models efficiently and reliably in production. Success is determined by how many users, agents, and workloads your AI infrastructure can serve without sacrificing performance or driving up costs.
TL;DR
Disaggregated inference is an AI-serving architecture that runs the two phases of LLM inference, prefill and decode, on separate specialized hardware instead of one accelerator.
Prefill is compute-bound and decode is memory-bandwidth-bound, so dedicating different hardware to each phase beats forcing one chip to do both.
Disaggregated inference scales prefill and decode independently, raising hardware utilization, lowering latency, and serving more workloads from the same infrastructure.
