Grace Hopper Superchip it has 72 ARM Neoverse V2 cores in the CPU part (Grace CPU), each of which has 64 KB of instruction and 64 KB of data L1 cache, and each core is also equipped with 1 MB of L2 cache. As for the L3 cache, it is shared and its capacity is 117 MB. LPDDR5X memory with a throughput of up to 512 GB/s is used, its capacity can reach 480 GB. It is possible to create 4 PCIe Gen5 x16 connections per processor.
The GPU part of this superchip (Hopper H100 GPU) manages to achieve 67 TFLOPS in FP32, 494 TFLOPS in TF32 Tensor Core, 1979 TFLOPS in FP8 (3958 TFLOPS using sparse matrices). Here we have up to 96 GB of HBM3 memory with a throughput of 4 TB/s, which surpasses desktop cards like the RTX 4090 by more than 3 times. NVLink-C2C between GPU and CPU achieves a throughput of 900 GB/s. Nvidia says that this is a 7 times faster connection than using the PCIe bus, and all this at a fifth of the consumption of the communication interface. The TDP of the superchip (CPU+GPU+Memory) is programmable between 450 and 1000 W. All this will increase the AI training speed by 9 times, AI algorithm processing by 30 times compared to the previous generation.
And now back to the DGX GH200 supercomputer. It can connect up to 256 of these superchips, which brings a huge performance of 1 exaFLOPS. The supercomputer can be equipped with up to 144 TB of shared memory (480 GB from the CPU + 96 GB from the GPU, all 256 times). The first companies and projects to have access to the DGX GH200 will be Google Cloud, Meta and Microsoft. Nvidia will also build one supercomputer for itself and use it for development and for its own development teams. It will be called Nvidia Helios, which will consist of 4 DGX GH200 supercomputers at once. These will be connected via the Nvidia Quantum-2 InfiniBand bus. The result will be 1024 Grace Hopper Superchips.
Source: Svět hardware by www.svethardware.cz.
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