It's a superchip built to handle the world’s most complex generative AI workloads.
its next-generation GH200 Grace Hopper Superchip platform this week at SIGGRAPH, a computer graphics conference. The chips, the company explained in a press release, were “created to handle the world’s most complex generative AI workloads, spanning large language models, recommender systems and vector databases.” In other words, they’re designed to do the billions of tiny calculations that these AI systems require as quickly and efficiently as possible.
The GH200 is a successor to the H100, Nvidia’s most powerful current-generation AI-specific chip. The GH200 will use the same GPU but have 141 GB of memory compared to the 80 GB available on the H100. The GH200 will also be available in a few other configurations, including a dual configuration that combines two GH200s that will provide “3.5x more memory capacity and 3x more bandwidth than the current generation offering.
Chips like the GH200 are important for both training and running AI models. When AI developers are creating a new LLM or other AI model, dozens or hundreds of GPUs are used to crunch through the massive amount of training data. Then, once the model is ready, more GPUs are required to run it. The additional memory capacity will allow each GH200 to run larger AI models without needing to split the computing workload up over several different GPUs.
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