As enterprises seek alternatives to concentrated GPU markets, demonstrations of production-grade performance with diverse hardware reduce procurement risk.

INT8 provides better performance with comparable precision than floating point for AI inference. But when INT8 is unable to meet the desired performance with limited resources, INT4 optimization is ...

Team behind LMCache, the open-source caching project powering WEKA, Redis, and others, launches with $4.5M seed funding and releases beta product SAN FRANCISCO--(BUSINESS WIRE)--Tensormesh, the ...

As AI workloads move from centralised training to distributed inference, the industry’s fibre infra challenge is changing ...

Posts from this topic will be added to your daily email digest and your homepage feed. ‘That’s 100 gigawatts of inference compute, distributed all around the world,’ Musk said. ‘That’s 100 gigawatts ...

The next generation of inference platforms must evolve to address all three layers. The goal is not only to serve models efficiently, but also to provide robust developer workflows, lifecycle ...

Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...

FPGAs might not have carved out a niche in the deep learning training space the way some might have expected but the low power, high frequency needs of AI inference fit the curve of reprogrammable ...