About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Snowflake Releases Arctic Open-Source LLM for Complex Enterprise Workloads

Subscribe to our newsletter

Snowflake has released Arctic, an open-source large language model (LLM) designed to deliver top-tier intelligence and efficiency at scale, and optimised for complex enterprise workloads. The company’s debut of its own open-source LLM takes it into competition with the likes of OpenAI’s GPT-4, Google’s Gemini, Meta Platforms’ Llama 2 and Mistral AI’s Mixtral.

Snowflake Arctic is based on a ‘mixture-of-experts’ architecture and is part of the company’s LLM family that includes practical text-embedding models for retrieval use cases. Underlining the openness of the LLM, Snowflake is releasing Arctic’s weights under an Apache 2.0 license and details of the research leading to how it was trained.

“By delivering intelligence and efficiency in a truly open way to the AI community, we are furthering what open-source AI can do. Our research with Arctic will enhance our capability to deliver reliable, efficient AI to our customers,” says Sridhar Ramaswamy, CEO at Snowflake.

Snowflake Arctic includes code templates and flexible inference and training options so users can get started quickly with deploying and customising Arctic using their preferred frameworks. These will include NVIDIA NIM with NVIDIA TensorRT-LLM, vLLM, and Hugging Face, a machine learning and data science platform and community that helps users build, deploy and train machine learning models, and provides infrastructure to demo, run and deploy AI in live applications.

For immediate use, Arctic is available for serverless inference in Snowflake Cortex, Snowflake’s fully managed service that offers machine learning and AI solutions in the Data Cloud. It will also be available on Amazon Web Services, Microsoft Azure, Hugging Face, Lamini, NVIDIA API catalog, Perplexity and Together AI. When accessed in Snowflake Cortex, Arctic will accelerate customers’ ability to build production-grade AI apps at scale, within the security and governance perimeter of the Data Cloud.

Clement Delangue, CEO and co-founder at Hugging Face, concludes: “There has been a massive wave of open-source AI in the past few months. Snowflake is contributing significantly with this release not only of the model with an Apache 2.0 license but also with details on how it was trained. It gives the necessary transparency and control for enterprises to build AI and for the field as a whole to break new ground.”

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: The ROI of Data Trust: Quantifying the Business Value of Data Observability

Date: 8 July 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes Data is the fuel that keeps modern financial institutions’ motors running but if that data can’t be trusted then the decisions made based upon it, or the uses to which its put, will be compromised. That’s especially important for...

BLOG

73 Strings QnA: Solving Post-Investment Data Challenges for Private Markets

Paris-based startup 73 Strings was established to modernise the data and valuation infrastructure for private market participants. Data Management Insight spoke to founder and chief executive Yann Magnan about the company’s operations and its ambitions. Data Management Insight: Hello Yann, when was 73 Strings created and how does it serve financial institutions? Yann Magnan: We...

EVENT

Eagle Alpha Alternative Data Conference, Fall, New York, hosted by A-Team Group

Now in its 8th year, the Eagle Alpha Alternative Data Conference managed by A-Team Group, is the premier content forum and networking event for investment firms and hedge funds.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...