As NVIDIA Approaches $1T, Company Announces AI Tech Needed To Surpass $2T

Now, Jensen has announced the DGX GH200 massive-memory supercomputer for generative AI. Powered by Grace Hopper and NVLink to train large AI models and drive AI innovation forward. What DGX did for smaller AI, the GH200 will do for massive AI builders. The GH200 is interconnected with NVLink to provide 1 exaflop of AI performance and 144 terabytes of shared memory — nearly 500x more than the previous generation NVIDIA DGX A100, introduced in 2020.

Massive AI needs the performance and lossless packet delivery of Infiniband, but prefers the lower-cost and ubiquitous Ethernet networking to run its data centers. As the figure below shows in the upper right, Ethernet’s bandwidth fluctuates considerably as the TCP/IP protocol is durable to frequent packet drops. And thats just not ok with big AI.NVIDIA

NVIDIA’s solution is to provide these customers with Spectrum-X, a combination of a new “Spectrum-4” Ethernet switch combined with the high performance BlueField-3 DPU. The combination of the Spectrum-4 switch, the BlueField NIC, and the NVIDIA networking software stack achieves 1.7x better overall AI performance and power efficiency, along with the consistency afforded by a lossless network. Thats right: NVIDIA is promising an Ethernet network that.

Could this change? Yes. Competition will always nibble at NVIDIA’s heels. AMD has a serious entry into the market coming soon, the MI300, later this year. RISC-V solutions like Tenstorrent and Esperanto are getting attention and traction, but not in the market where Jensen is focussed: massive Foundation Models. Intel could pull a rabbit out of the hat with Gaudi3 and/or the forever-late PonteVecchio GPU.

But as I have said in the past, considering NVIDIA’s superior hardware combined with the depth and breadth of NVIDIA’s software to optimize AI and HPC applications, all competitors combined could maybe get 10% of the market. In a $75B market, that could be plenty to float some more boats.

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