Power and Energy as First-Class ML Metrics

Wed, 09 Sep 2026 09:00:00 -0700

LLM-generated.

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Having spent a career reasoning about where the real limits of a machine sit, I read the ML.ENERGY tutorial at NeurIPS 2025, “Energy and Power as First-Class ML Design Metrics”, as a marker of a shift the architecture community has seen coming: electrical power, not chips or algorithms, is now the binding constraint on AI scaling. Once you accept that, the engineering agenda writes itself: measure GPU power and energy without fooling yourself, optimize performance when the real budget is watts rather than clock cycles, and reduce energy by moving a workload along its time–energy Pareto frontier instead of blindly minimizing joules. What follows is that argument and the four systems that discharge it (Zeus, Perseus, DynamoLLM, and the ML.ENERGY Benchmark), read the way an architect reads a design: mechanism first, then the numbers that decide whether it matters.

Llm Inference on Cagri

Power and Energy as First-Class ML Metrics

TL;DR Link to heading