Snowflake Teams Up with Meta to Host and Optimize New Flagship Model Family in Snowflake Cortex AI
Snowflake's AI Research Team, in collaboration with the open source community, launches a Massive LLM Inference and Fine-Tuning System Stack — establishing a new state-of-the-art solution for open source inference and fine-tuning systems for multi-hundred billion parameter models like Llama 3.1 405B
Snowflake (NYSE:SNOW), the AI Data Cloud company, today announced that it will host the Llama 3.1 collection of multilingual open source large language models (LLMs) in Snowflake Cortex AI for enterprises to easily harness and build powerful AI applications at scale. This offering includes Meta's largest and most powerful open source LLM, Llama 3.1 405B, with Snowflake developing and open sourcing the inference system stack to enable real-time, high-throughput inference and further democratize powerful natural language processing and generation applications. Snowflake's industry-leading AI Research Team has optimized Llama 3.1 405B for both inference and fine-tuning, supporting a massive 128K context window from day one, while enabling real-time inference with up to 3x lower end-to-end latency and 1.4x higher throughput than existing open source solutions. Moreover, it allows for fine-tuning on the massive model using just a single GPU node — eliminating costs and complexity for developers and users — all within Cortex AI.
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Snowflake Teams Up with Meta to Host and Optimize New Flagship Model Family in Snowflake Cortex AI (Graphic: Business Wire)
By partnering with Meta, Snowflake is providing customers with easy, efficient, and trusted ways to seamlessly access, fine-tune, and deploy Meta's newest models in the AI Data Cloud, with a comprehensive approach to trust and safety built-in at the foundational level.
"Snowflake's world-class AI Research Team is blazing a trail for how enterprises and the open source community can harness state-of-the-art open models like Llama 3.1 405B for inference and fine-tuning in a way that maximizes efficiency," said Vivek Raghunathan, VP of AI Engineering, Snowflake. "We're not just bringing Meta's cutting-edge models directly to our customers through Snowflake Cortex AI. We're arming enterprises and the AI community with new research and open source code that supports 128K context windows, multi-node inference, pipeline parallelism, 8-bit floating point quantization, and more to advance AI for the broader ecosystem."
Snowflake's Industry-Leading AI Research Team Unlocks the Fastest, Most Memory Efficient Open Source Inference and Fine-Tuning
Snowflake's AI Research Team continues to push the boundaries of open source innovations through its regular contributions to the AI community and transparency around how it is building cutting-edge LLM technologies. In tandem with the launch of Llama 3.1 405B, Snowflake's AI Research Team is now open sourcing its Massive LLM Inference and Fine-Tuning System Optimization Stack in collaboration with DeepSpeed, Hugging Face, vLLM, and the broader AI community. This breakthrough establishes a new state-of-the-art for open source inference and fine-tuning systems for multi-hundred billion parameter models.
Massive model scale and memory requirements pose significant challenges for users aiming to achieve low-latency inference for real-time use cases, high throughput for cost effectiveness, and long context support for various enterprise-grade generative AI use cases. The memory requirements of storing model and activation states also make fine-tuning extremely challenging, with the large GPU clusters required to fit the model states for training often inaccessible to data scientists.
Snowflake's Massive LLM Inference and Fine-Tuning System Optimization Stack addresses these challenges. By using advanced parallelism techniques and memory optimizations, Snowflake enables fast and efficient AI processing, without needing complex and expensive infrastructure. For Llama 3.1 405B, Snowflake's system stack delivers real-time, high-throughput performance on just a single GPU node and supports a massive 128k context windows across multi-node setups. This flexibility extends to both next-generation and legacy hardware, making it accessible to a broader range of businesses. Moreover, data scientists can fine-tune Llama 3.1 405B using mixed precision techniques on fewer GPUs, eliminating the need for large GPU clusters. As a result, organizations can adapt and deploy powerful enterprise-grade generative AI applications easily, efficiently, and safely.
Snowflake's AI Research Team has also developed optimized infrastructure for fine-tuning inclusive of model distillation, safety guardrails, retrieval augmented generation (RAG), and synthetic data generation so that enterprises can easily get started with these use cases within Cortex AI.
Snowflake Cortex AI Furthers Commitment to Delivering Trustworthy, Responsible AI
AI safety is of the utmost importance to Snowflake and its customers. As a result, Snowflake is making Snowflake Cortex Guard generally available to further safeguard against harmful content for any LLM application or asset built in Cortex AI — either using Meta's latest models, or the LLMs available from other leading providers including AI21 Labs, Google, Mistral AI, Reka, and Snowflake itself. Cortex Guard leverages Meta's Llama Guard 2, further unlocking trusted AI for enterprises so they can ensure that the models they're using are safe.
Comments on the News from Snowflake Customers and Partners
"As a leader in the hospitality industry, we rely on generative AI to deeply understand and quantify key topics within our Voice of the Customer platform. Gaining access to Meta's industry-leading Llama models within Snowflake Cortex AI empowers us to further talk to our data, and glean the necessary insights we need to move the needle for our business," said Dave Lindley, Sr. Director of Data Products, E15 Group. "We're looking forward to fine-tuning and testing Llama to drive real-time action in our operations based on live guest feedback."
"Safety and trust are a business imperative when it comes to harnessing generative AI, and Snowflake provides us with the assurances we need to innovate and leverage industry-leading large language models at scale," said Ryan Klapper, an AI leader at Hakkoda. "The powerful combination of Meta's Llama models within Snowflake Cortex AI unlocks even more opportunities for us to service internal RAG-based applications. These applications empower our stakeholders to interact seamlessly with comprehensive internal knowledge bases, ensuring they have access to accurate and relevant information whenever needed."
"By harnessing Meta's Llama models within Snowflake Cortex AI, we're giving our customers access to the latest open source LLMs," said Matthew Scullion, Matillion CEO and co-founder. "The upcoming addition of Llama 3.1 gives our team and users even more choice and flexibility to access the large language models that suit use cases best, and stay on the cutting-edge of AI innovation. Llama 3.1 within Snowflake Cortex AI will be immediately available with Matillion on Snowflake's launch day."
"As a leader in the customer engagement and customer data platform space, Twilio's customers need access to the right data to create the right message for the right audience at the right time," said Kevin Niparko VP, Product and Technology Strategy, Twilio Segment. "The ability to choose the right model for their use case within Snowflake Cortex AI empowers our joint customers to generate AI-driven, intelligent insights and easily activate them in downstream tools. In an era of rapid evolution, businesses need to iterate quickly on unified data sets to drive the best outcomes."
Learn More:
- For enterprises interested in distilling Llama 3.1 405B for their domain-specific use cases and getting additional support from Snowflake's AI Research Team, fill out this form.
- More details on how to get started with Llama 3.1 405B and Snowflake Cortex AI can be found in this quickstart guide.
- Double click into the various ways developers can harness Llama 3.1 405B within Snowflake Cortex AI in this blog post.
- Dive into the technical details of how Snowflake's AI Research Team is enabling efficient and cost-effective inference, alongside the fine-tuning of massive multi-hundred billion parameter models.
- Learn more about the continued innovation coming out of Snowflake's AI Research Team, and meet the experts driving the future of AI forward in the AI Research hub.
- Stay on top of the latest news and announcements from Snowflake on LinkedIn and Twitter / X.
Forward Looking Statements
This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.
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