Nvidia L4 for AI Video is designed to deliver 120 times more AI-powered video performance than CPUs, while Grace Hopper for Recommendation Models is built with graph recommendation models in mind, as well as vector databases and graph neural networks.Īccording to Nvidia, the H100 NVL for Large Language Model Deployment is “ideal for deploying massive LLMs like ChatGPT at scale.” With 94GB of memory via its Transformer Engine, the platform provides up to 12 times faster inference performance at GPT-3 compared to prior generation A100s at a data center scale. Each is optimized for workloads including AI video, image generation, large language model deployment and recommender inference.

The platforms combine Nvidia software with processors including the L4 Tensor Core GPU and the H100 NVL GPU. Sticking with hardware, Nvidia also launched four inference platforms for generative AI applications. Related: Nvidia GTC ’23: The Next Chapter of Generative AI This follows Microsoft Azure’s private preview announcement last week for its H100 virtual machine, ND H100 v5.

AWS’s forthcoming EC2 UltraClusters of Amazon EC2 P5 instances are also set to feature H100s.

Oracle will offer ‘limited availability’ of its Compute bare-metal GPU instances featuring H100s. It would not be a GTC without an update on chips: Among the biggest semiconductor news at GTC was that vendors including Oracle Cloud Infrastructure, AWS and Microsoft Azure are offering new products and services featuring the new Nvidia H100 Tensor Core GPU, its recently released AI hardware.Īccording to Nvidia, the H100 ‘Hopper’ Tensor Core GPUs are up to 4.5 times faster than its current fastest AI chip, the A100.

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