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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 ...
The purpose of this note is to help mainstream fiscal multipliers in PFRs. It aims to provide guidance for estimating fiscal ...
As Enterprise AI matures from experimental chatbots to production-grade Agentic workflows, a silent infrastructure crisis is the VRAM bottleneck. Deploying a dedicated endpoint for every fine-tuned ...
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