AI gives every company the opportunity to turn its processes into a data flywheel, NVIDIA founder and CEO Jensen Huang told thousands of conference attendees on Monday. Snowflake Data Cloud Summit.
Companies should “take all the most important processes they do, capture them in a data flywheel, and turn them into enterprise AI to drive that flywheel even further,” said Huang, joining from Taipei for a virtual fireside chat with Snowflake CEO Sridhar Ramaswamy in San Francisco.
The two executives described how the combination of Snowflake AI Data Cloud and NVIDIA AI will simplify and accelerate enterprise AI.
“If you want to get on this train as fast as you can, don't miss it because you can use it to transform your business or start new businesses,” said Huang, the day after giving a keynote starting COMPUTEX in Taiwan.
Snowflake users can take advantage of NVIDIA AI Enterprise
For example, companies will be able to deploy Snowflake Arctic, a large enterprise-focused language model (LLM), in seconds using NVIDIA NIM inference microservices, part of the NVIDIA AI Company software platform.
The Arctic was trained in NVIDIA H100 Tensor Core GPU and is available in the NVIDIA API Catalogfully supported by NVIDIA TensorRT-LLMsoftware that accelerates AI generative inference.
The two companies will also integrate Snowflake Bark AI and NVIDIA NeMo Recoveryso that companies can link their AI-powered applications to information sources, ensuring highly accurate results with increased recall generation (RAG).
Ramaswamy gave examples of generative AI applications developed with the NVIDIA NeMo frame and Container services for snow parks which will be available in snowflake market for use by thousands of Snowflake customers.
“NVIDIA's industry-leading accelerated computing is a game-changer for our customers and our own research team who used it to create the next-generation Artic model for our customers,” said Ramaswamy.
For more information, watch the NVIDIA GTC on-demand sessions presented by Snowflake on how to build chatbots with a RAG architecture and How to take advantage of LLMs for life sciences.
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