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Home News NVIDIA releases a DGX GH200 supercompute...

NVIDIA releases a DGX GH200 supercomputer that integrates 256 GH200 chips, and the Grace Hopper supercomputer has been put into production

NVIDIA releases a DGX GH200 supercomputer that integrates 256 GH200 chips, and the Grace Hopper supercomputer has been put into production

Nvidia released several heavyweight news at the 2023 Computer Show today, with the most notable being that its Grace Hopper superchip has been fully put into production. These chips are the core components of NVIDIA's newly launched DGX GH200 artificial intelligence supercomputing platform and MGX system, designed specifically to handle massive generative artificial intelligence tasks. Nvidia also announced its new Spectrum X Ethernet network platform, optimized for artificial intelligence servers and supercomputing clusters.
The Grace Hopper Superchip is a CPU+GPU integration solution developed by NVIDIA based on the Arm architecture. It integrates 72 core Grace CPU, Hopper GPU, 96GB HBM3, and 512 GB LPDDR5X in the same package, with a total of 200 billion transistors. This combination provides astonishing data bandwidth between CPU and GPU, up to 1 TB/s, providing significant advantages for certain memory constrained workloads.
NVIDIA releases a DGX GH200 supercomputer that integrates 256 GH200 chips, and the Grace Hopper supercomputer has been put into production

The DGX GH200 Artificial Intelligence Supercomputing Platform is a system and reference architecture designed by Nvidia for the most high-end artificial intelligence and high-performance computing workloads. Currently, the DGX A100 system can only combine eight A100 GPUs as a unit. Considering the explosive growth of generative artificial intelligence, Nvidia's customers urgently need larger and more powerful systems, The DGX GH200 is designed to provide maximum throughput and scalability by using NVIDIA's custom NVLink Switch chip to avoid the limitations of standard cluster connection options such as InfiniBand and Ethernet.


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