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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.
3d
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 European Commission is trialling using European open source software to run its internal communications, a spokesperson confirmed to Euractiv. The move comes at a time of growing concern within ...
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Tahoe Therapeutics generates the largest single-cell atlas ever using INTEGRA Biosciences automated pipetting technologies
Tahoe Therapeutics - a biotech start-up based in San Francisco, California - is creating the largest ever atlas of ...
Abstract: Sparse arrays have gained increasing research attention due to their potential to reduce system cost and weight. However, current studies on sparse array synthesis often overlook the ...
Abstract: In order to improve the efficiency of inverter-driven motors, it is required to prevent partial discharge, and it has been pointed out that PDIV may be lowered during operation due to the ...
Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly evaluated by ...
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