How distributed inference keeps large AI models moving

IBM Research · 12 October 2026, 04:00

llm-d’s distributed inference framework coordinates clusters of model servers so large LLM inference can handle many concurrent requests, reuse shared prompt context, and manage overload. In a demo, prefix-aware routing captured 97% of the available cache reuse in the tested workload. As a result, redundant prefill work drops and traffic is paced and routed more efficiently across servers, including when requests arrive in bursts from AI agents.

Why it matters

Distributed inference serving is an air traffic control tower for AI traffic, and agentic workflows make it more necessary than ever.