Snowflake, the AI Data Cloud company, 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.
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.
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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.
Source: Businesswire
