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New ATEST framework optimizes LLM inference by adapting to GPU load and energy costs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ATEST, a novel framework for adaptive resource-aware test-time scaling in large language model inference. This system addresses the issue of fixed sampling budgets in traditional methods by dynamically adjusting the inference effort based on query difficulty and real-time GPU conditions, including energy costs. ATEST-Online, a mode that requires no offline calibration, incrementally generates samples, monitors GPU load, and learns energy costs to determine when to stop, demonstrating significant improvements in accuracy and energy efficiency under varying loads. AI

IMPACT Optimizes LLM inference efficiency by dynamically adapting to real-time resource constraints and energy costs.

RANK_REASON The item is a research paper detailing a new framework for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ATEST framework optimizes LLM inference by adapting to GPU load and energy costs

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The item is a research paper detailing a new framework for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    ATEST: Adaptive Resource-Aware Test-Time Scaling for LLM Inference

    Test-time scaling (TTS) improves large language model (LLM) reasoning by sampling multiple answers, but fixed sampling budgets ignore both query difficulty and the changing energy cost of inference under GPU contention. We measure up to a 4.1-fold increase in energy for the same …

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