Optimizing LLMs for Energy Efficiency

Описание и применение

Оптимизация энергоэффективности LLM через измерение латентности и выбор подходящей модели.

Задача

API-based LLM inference consumes significant energy, leading to higher latency, infrastructure costs, and limited workload capacity.

Результат

By measuring latency proxies and selecting appropriately sized models, teams can cut energy consumption per request, reduce latency, and lower infrastructure spend while maintaining output quality.

Рабочий процесс

• 1. Instrument client-side metrics (TTFT, TPOT, total latency) using a wrapper around the OpenAI SDK.
• 2. Run baseline measurements on candidate models to establish latency benchmarks.
• 3. Evaluate models across the Oxlo.ai catalog, selecting the smallest model that meets quality thresholds.
• 4. Compare energy proxies (median latency) to identify the most efficient model for each task.
• 5. Iterate and refine prompts or model choices based on measured trade‑offs.

Доказательства

Measure these client‑side metrics: TTFT, TPOT, End‑to‑end latency for a fixed prompt set.

Источник

Oxlo.ai hosts a spectrum of architectures, from the 1M‑context efficient MoE DeepSeek V4 Flash to the general‑purpose Llama 3.3 70B and the advanced reasoning Kimi K2.6.

Источник