In a world where allocations of “Hopper” H100 GPUs coming out of Nvidia’s factories are going out well into 2024, and the allocations for the impending “Antares” MI300X and MI300A GPUs are probably long since spoken for, anyone trying to build a GPU cluster to power a large language model for training or inference has to think outside of the box. …
To hardware or not to hardware, that is a real question for vendors peddling all kinds of software in the datacenter. …
Supercomputers are expensive, and getting increasingly so. Even if they are delivering impressive performance gains over the past decade, modern HPC workloads require an incredible amount of performance, and this is particularly true of any workload that is going to blend together traditional HPC simulation and modeling with some sort of machine learning training and inference. …
If they are doing their jobs right, the high performance computing centers around the world in academic and government institutions are supposed to be on the cutting edge of any new technology that boosts the performance of simulation, modeling, analytics, and artificial intelligence. …
Maximizing the aggregate amount of compute that can be brought to bear for any given pile of money is what traditional high performance computing is all about. …
Stranded capacity has always been the biggest waste in the datacenter, and over the years, we have added more and more clever kinds of virtualization – hardware partitions, virtual machines and their hypervisors, and containers – as well as the systems management tools that exploit them. …
Hardware is, by its very nature, physical and therefore, unlike software or virtual hardware and software routines encoded by FPGAs, it is the one thing that cannot be easily changed. …
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