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Fp64 use cases6/18/2023 ![]() It supports error-correction code (ECC) and twice the memory bandwidth of traditional DDR5 memory, for 1TBps of throughput.Īlong with Hopper, Nvidia announced NVLink 4, its latest GPU-to-GPU interconnect. It also modified low-power double data rate (LPDDR) 5 memory-normally used in smart phones-to create LPDDR5X. But Nvidia will also pair it with a CPU on a custom Arm processor called Grace that it developed and expects have available in 2023.įor Hopper, Nvidia did more than just amp up the GPU processor. Like previous GPUs, the Hopper H100 GPU can operate as a standalone processor running on an add-in PCI Express board in a server. Nvidia says the Hopper H100 will top out at 60TFLOPS of FP64 performance. Nvidia laid out its GPU roadmap for the year in March with the announcement of its Hopper GPU architecture, claiming that, depending on use, it can deliver three to six times the performance of its previous architecture, Ampere, which weighs in at 9.7 TFLOPS of FP64. Here’s a look at what Nvidia, AMD, and Intel have in store. Nvidia, AMD, and Intel have laid their cards on the table about their immediate plans, and it looks like this will be a stiff competition. So what does the year hold for server GPUs? Quite a bit as it turns out. This number sometimes specifies the standardized floating-point format in use when the measure is made, such as FP64. Performance of GPUs is measured in how many of these floating-point math operations they can perform per second or FLOPS. By contrast Nvidia’s current GPU generation, Ampere, has 6,912 cores, all operating in parallel to do one thing: math processing, specifically floating-point math. For example, Intel’s Xeon server CPUs have up to 28 cores, while AMD’s Epyc server CPUs have up to 64. ![]() To accomplish this, they have multiple cores, many more than the general-purpose CPU. CPUs can handle the work it just takes them longer.īecause GPUs are designed to solve complex mathematical problems in parallel by breaking them into separate tasks that they work on at the same time, they solving them more quickly. That’s because GPUs are better suited than CPUs for handling many of the calculations required by AI and machine learning in enterprise data centers and hyperscaler networks. These three vendors recognize the demand for GPUs in data centers as a growing opportunity.
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