Snowflake Expands Collaboration With Nvidia To Deliver Full-Stack AI Platform For Enterprises

Data cloud company Snowflake Inc. has expanded its collaboration with Nvidia Corp. to deliver enterprises a full-stack artificial intelligence platform that integrates Nvidia’s accelerated computing with Snowflake’s Data Cloud.

The combination of infrastructure and compute capabilities aims to accelerate AI productivity and enable business transformations across industries.

With previously announced Nvidia NeMo integration, Snowflake customers will be able to utilise NeMo Retriever directly on their proprietary data in the Data Cloud, while maintaining data security, privacy and governance, Snowflake said.

“Data is the fuel for AI, making it essential to establishing an effective AI strategy,” said Snowflake chief executive officer Sridhar Ramaswamy. ". Our partnership with Nvidia is delivering a secure, scalable and easy-to-use platform for trusted enterprise data."

NeMo Retriever enables scalability of chatbot applications and can accelerate time to value for enterprises building AI applications with Snowflake Cortex, a large language model and vector search service, the company said.

The expanded collaboration will also include the availability of Nvidia TensorRT software, aimed at delivering low latency and high throughput for deep learning inference applications to enhance LLM-based search capabilities.

“Enterprise data is the foundation for custom AI applications that can generate intelligence and reveal new insights,” said Jensen Huang, founder and chief executive officer of Nvidia. “Bringing Nvidia accelerated computing and software to Snowflake’s data platform can turbocharge enterprise AI adoption by helping customers build, deploy and manage secure generative AI applications.”

According to Snowflake, several of its AI products are powered by Nvidia’s accelerated computing, including:

• Snowflake Cortex LLM Functions: The product allows users with SQL skills to leverage smaller LLMs to cost-effectively address specific tasks, including sentiment analysis, translation and summarisation. More advanced use cases include the development of AI applications using performant models from Mistral AI, Meta and others.

• Snowflake Copilot: The LLM-powered assistant leverages generative AI for coding tasks with natural language. Users can ask questions of their data in plain text, write SQL queries against relevant data sets, refine queries and filter down insights.

• Document AI: Document AI helps enterprises use LLMs to extract content like invoice amounts or contractual terms from documents and fine-tune results using a visual interface and natural language. It can help teams reduce manual errors and increase efficiencies with automated document processing.