llm-d: Breaking the Cost and Capacity Barriers
Red Hat, Tuesday, August 18th, 2026
Red Hat argues inference efficiency now drives AI cost, and positions llm-d as the way to use scarce capacity well.
Red Hat argues the AI industry is shifting attention from training models to running them efficiently. Enterprise AI applications generate millions of inference requests as they coordinate multiple models, tools and agents, and every request consumes computing capacity.
That makes inference efficiency a primary driver of both performance and infrastructure cost. The challenge is twofold: acquiring enough capacity and using it intelligently once acquired. Red Hat positions llm-d as addressing the second problem, which it argues matters more as model parameter counts grow.