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

Hugging Face Daily Papers Papers
llm inference energy-efficiency token-production evaluation metrics

Summary

This paper argues that LLM inference should be evaluated as energy-to-token production under constraints of compute, power, cooling, and operational efficiency, proposing new metrics like joules/token and PUE-adjusted delivered power.

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, delivered data-center power, cooling capacity, PUE, and utilization. We argue that the ML community should treat inference as energy-to-token production. We formalize this view with a dimensionally consistent Token Production Function in which token rate is bounded by both compute-per-token and energy-per-token ceilings. Listed API prices vary by over an order of magnitude across providers, but we use price dispersion only as directional motivation, not as causal evidence of marginal cost. The core physical question is instead: under fixed quality and service targets, when does the binding constraint move from theoretical peak compute toward delivered power, cooling, and operational efficiency? Under this framing, system optimizations -- latent KV-cache compression, sparse or heavily compressed attention, quantization, routing, and difficulty-adaptive reasoning -- are not merely local engineering tricks. They are energy-to-token levers because they reduce FLOPs/token, joules/token, memory traffic, or utilization losses under fixed (q^{*},s^{*}). We therefore call for inference papers and benchmarks to report Joules/token, active binding constraint, PUE-adjusted delivered power, and utilization-adjusted token output alongside accuracy and latency.
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Source: https://huggingface.co/papers/2605.11733

Abstract

LLM inference should be evaluated as energy-to-token production under constraints of compute, power, cooling, and operational efficiency, requiring new metrics beyond traditional accuracy and latency measures.

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, delivered data-center power, cooling capacity,PUE, and utilization. We argue that the ML community should treat inference asenergy-to-token production. We formalize this view with a dimensionally consistentToken Production Functionin which token rate is bounded by both compute-per-token and energy-per-token ceilings. Listed API prices vary by over an order of magnitude across providers, but we use price dispersion only as directional motivation, not as causal evidence of marginal cost. The core physical question is instead: under fixed quality and service targets, when does the binding constraint move from theoretical peak compute towarddelivered power, cooling, and operational efficiency? Under this framing, system optimizations --latent KV-cache compression, sparse or heavily compressed attention,quantization,routing, anddifficulty-adaptive reasoning-- are not merely local engineering tricks. They are energy-to-token levers because they reduceFLOPs/token,joules/token, memory traffic, or utilization losses under fixed (q^{*},s^{*}). We therefore call for inference papers and benchmarks to reportJoules/token, active binding constraint,PUE-adjusteddelivered power, andutilization-adjusted token outputalongside accuracy and latency.

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