Enabling Customized LLMs for Accelerated AI Applications in Data Cloud
New partnership allows companies to securely create custom large language models (LLMs) without moving data
Snowflake has partnered with NVIDIA to provide businesses with an accelerated path to securely build custom large language models (LLMs) using their own proprietary data.
According to the company, the integration with NVIDIA’s NeMo platform and GPU-accelerated computing will allow enterprises to quickly deploy and manage customized LLMs for advanced generative AI services, including chatbots, search, and summarization. The collaboration is also expected to help customers reduce cost and latency while keeping their data secure within the Snowflake Data Cloud.
“Snowflake’s partnership with NVIDIA will bring high-performance machine learning and artificial intelligence to our vast volumes of proprietary and structured enterprise data, a new frontier to bringing unprecedented insights, predictions and prescriptions to the global world of business,” said Frank Slootman, Chairman and CEO of Snowflake.
Through the partnership, the two companies aim to bring customized generative AI applications to various industries using the Data Cloud. For instance, a healthcare insurance model could provide answers to complex questions about coverage under different plans, while a financial services model could offer details about lending opportunities based on specific circumstances. Developers can ensure that their applications align with business-specific topics, safety, and security requirements using the NeMo Guardrails software.
“Data is essential to creating generative AI applications that understand the complex operations and unique voice of every company,” said Jensen Huang, founder and CEO, NVIDIA. “Together, NVIDIA and Snowflake will create an AI factory that helps enterprises turn their own valuable data into custom generative AI models to power groundbreaking new applications — right from the cloud platform that they use to run their businesses.”
Snowflake has also introduced new advancements to its unified platform, focusing on making data utilization easier for organizations and enhancing performance for critical workloads. The key advancements include the launch of a new LLM developed using Applica’s generative AI technology. This LLM will enable customers to understand and leverage unstructured data by analyzing documents.
Additionally, the company is introducing updates to Iceberg Tables to further eliminate data silos and allow firms to use open table formats with fast performance and enterprise-grade governance. The Snowflake Performance Index (SPI) is another significant introduction, offering increased transparency and metrics regarding the company’s ongoing performance improvements. According to the SPI, query duration for stable customer workloads has improved by 15% between August 25, 2022, and April 30, 2023.
According to an IDC White Paper titled “Meeting the New Unstructured Storage Requirements for Digitally Transforming Enterprises”, which was sponsored by Dell Technologies and released in October 2022, more than 90% of the world’s data is expected to be in unstructured formats like documents, images, video, and audio over the next five years. Snowflake addresses this challenge by integrating Document AI, a feature resulting from its acquisition of Applica, into its platform.
Document AI leverages a purpose-built, multimodal LLM to enable organizations to extract value from documents effortlessly using natural language processing. Through Document AI, users can extract specific content such as invoice amounts or contractual terms, fine-tune results, and enhance productivity in a secure and scalable manner. Snowflake plans to expand these capabilities to other types of unstructured data in the future.
In addition to these advancements, the company is expanding its single platform to support advanced analytics capabilities, including pre-built machine learning functions for SQL users (public preview), and enhanced unified governance and privacy features such as data quality metrics and classification (private preview).
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