With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.
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Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding
Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
A new research paper from Apple details a technique that speeds up large language model responses, while preserving output quality. Here are the details. Traditionally, LLMs generate text one token at ...
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