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The Token-Energy Law of LLM Decode: Active Bytes and Realized Time Account for Joules per Token

Chenxu Niu, Batuhan Sencer, Wei Zhang, Yong Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28576 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大規模な言語モデルのエネルギー効率的な展開には、モデルアーキテクチャ、ハードウェア、およびサービス設定が生成されたトークンごとにエネルギーを形成する方法を理解する必要がある。我々は、密集した、完全な歴史のデコードのためのGPUボードエネルギーのコンパクトで解釈可能なモデルであるトークンエネルギー法を導入する。それは、アクティブなバイトと実現されたデコード時間の2つのコア次元にトークンエネルギーを関連付ける。アクティブバイトは、出力ごとに論理的なバイト需要を記述し、デコードイテレーション内の密集した重量を個々のキー・値(KV)の歴史から読んで更新する。実現されたデコード

Machine translation (Meta M2M100); the English original follows.

Abstract

Energy-efficient deployment of large language models requires understanding how model architecture, hardware, and serving settings shape energy per generated token. We introduce the Token-Energy Law, a compact, interpretable model of GPU-board energy per output token for dense, full-history decoding. It relates token energy to two core dimensions: active bytes and realized decode time. Active bytes describe logical byte demand per output, separating dense weights shared within a decode iteration from private key--value (KV) histories read and updated for each request. Realized decode time is the elapsed decode duration divided by the number of output tokens produced. We combine the two byte coordinates and time in a common affine form, with coefficients calibrated for each model and hardware/runtime configuration. This formulation explains how batching amortizes shared-weight work, longer attended histories increase private KV demand, and throughput determines the time-associated cost per output. Our experiments cover three models under BF16 decoding on NVIDIA Hopper and Blackwell GPUs, including two-GPU tensor parallelism on Hopper. Using fixed coefficients and observed decode time, held-out condition-mean energy estimates differ from measurements by at most 3.73%.

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Topics: Byte, Security token, Computer science, Energy (signal processing), Decoding methods, Affine transformation

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