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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 ...
If alternative training pathways gain traction, universities are likely to adapt over time. That evolution could include coursework that blends manual and AI-enabled assignments, new classes on AI ...
Abstract: Semi-Supervised polyp segmentation has made significant progress in recent years as a potential solution for computer-assisted treatment. Since depth images can provide extra information ...
India and the UK are partnering to advance AI development. Both nations are committed to establishing shared values in AI. Collaboration will focus on infrastructure, including chips, and the ...
Arrcus launched a new network fabric layer targeted at potential traffic bottlenecks caused by the growing use of AI ...
Abstract: The rapid deployment of intelligent applications on edge cloud calls for efficient and responsive deep neural network inference, especially under the burst scenarios of inference request.
Physical AI is not merely a product feature. It is an architectural shift. The question before us is simple: Will the world of Physical AI be built by a few thousand engineers, or by millions of ...
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