Harini Muthukrishnan (U of Michigan); David Nellans, Daniel Lustig (NVIDIA); Jeffrey A. Fessler, Thomas Wenisch (U of Michigan). Abstract—”Despite continuing research into inter-GPU communication ...
Understanding GPU memory requirements is essential for AI workloads, as VRAM capacity--not processing power--determines which models you can run, with total memory needs typically exceeding model size ...
Nvidia Corp. today disclosed that it has acquired Run:ai, a startup with software for optimizing the performance of graphics card clusters. The terms of the deal were not disclosed. TechCrunch, citing ...
Crusoe, the industry’s first vertically integrated AI infrastructure provider, is announcing its acquisition of Atero, the company specializing in GPU management and memory optimization for AI ...
As enterprises seek alternatives to concentrated GPU markets, demonstrations of production-grade performance with diverse ...
GSI Gemini-I APU reduces constant data shuffling between the processor and memory systems Completes retrieval tasks up to 80% faster than comparable CPUs GSI Gemini-II APU will deliver ten times ...
The use of Graphics Processing Units (GPUs) to accelerate the Finite Element Method (FEM) has revolutionised computational simulations in engineering and scientific research. Recent advancements focus ...
Deciding on the correct type of GPU accelerated computation hardware depends on many factors. One particularly important aspect is the data flow patterns across the PCIe bus and between GPUs and ...
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