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Together AI introduces AutoJudge for faster LLM inference

By PulseAugur Editorial · [1 sources] ·

Researchers at Together AI have developed AutoJudge, a novel method to accelerate large language model inference. This technique automates the curation of task-specific datasets, enabling lossy speculative decoding without manual annotation. AutoJudge identifies critical tokens that impact downstream quality, achieving up to a 2x speedup over standard speculative decoding with minimal accuracy loss. AI

IMPACT Accelerates LLM inference by automating dataset curation for speculative decoding, potentially reducing operational costs.

RANK_REASON The cluster describes a new research paper detailing a novel method for LLM inference acceleration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Together AI blog →

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

Together AI introduces AutoJudge for faster LLM inference

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The cluster describes a new research paper detailing a novel method for LLM inference acceleration. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Together AI blog TIER_1 English(EN) ·

    Introducing AutoJudge: Streamlined inference acceleration via automated dataset curation

    AutoJudge accelerates LLM inference by identifying which token mismatches actually matter. Using self-supervised learning to train a lightweight classifier, it accepts up to 40 draft tokens per cycle—delivering 1.5–2× speedups over standard speculative decoding with minimal accur

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