Nvidia is not the only company that has created specialized compute units that are good at the matrix math and tensor processing that underpins AI training and that can be repurposed to run AI inference. …
There is increasing competition coming at Nvidia in the AI training and inference market, and at the same time, researchers at Google, Cerebras, and SambaNova are showing off the benefits of porting sections of traditional HPC simulation and modeling code to their matrix math engines, and Intel is probably not far behind with its Habana Gaudi chips. …
When Nvidia announced a deal to buy Mellanox Technologies for $6.9 billion in March 2019, everyone spent a lot of time thinking about the synergies between the two companies and how networking was going to become an increasingly important part of the distributed systems that run HPC and AI workloads. …
With each passing generation of GPU accelerator engines from Nvidia, machine learning drives more and more of the architectural choices and changes and traditional HPC simulation and modeling drives less and less. …
Within a year or so, with the launch of the “Grace” Arm server CPUs, it will not be heresy for anyone at Nvidia to believe, or to say out loud, that not every workload in the datacenter needs to have GPU acceleration. …
Normally, when we look at a system, we think from the compute engines at a very fine detail and then work our way out across the intricacies of the nodes and then the interconnect and software stack that scales it across the nodes into a distributed computing platform. …
There are two types of packaging that represent the future of computing, and both will have validity in certain domains: Wafer scale integration and multichip module packaging. …
The modern GPU compute engine is a microcosm of the high performance computing datacenter at large. …
The world would be a simpler place for all processing engine makers if they just had to make one device to cover all use cases, thus maximizing volumes and minimizing per unit costs. …
In a world where GPUs have tens of billions of transistors and chip manufacturing techniques are costly and yields are particularly tough because of the largesse of these devices, every chip that comes of out the foundry is sacred. …
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