The database company Snowflake is adding another large language model (LLM) into the AI ecosystem.
Snowflake Arctic is an LLM designed for complex enterprise workloads, with cost-effectiveness as a key highlight.
It can efficiently complete enterprise intelligence tasks like SQL generation, coding, and instruction following, meeting or exceeding benchmarks in those areas when compared to the models that had a much higher training budget than it needed. According to Snowflake, these metrics are important to businesses because those are the capabilities needed to build generative AI copilots.
Arctic is on par in terms of its enterprise intelligence with LLama 3 8B and Llama 2 70B, but used less than half of the training budgets as those models. It also was on par with Llama 3 70B, which had a training budget seventeen times higher.
According to Snowflake, the low training cost will enable companies to train custom models without needing to spend excessive amounts of money.
“Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars,” the Snowflake AI Research team wrote in a blog post.
In addition to being cost-effective, Arctic is open source under the Apache 2.0 license. The company also made its data recipes and research insights available to the public as well.
“By delivering industry-leading intelligence and efficiency in a truly open way to the AI community, we are furthering the frontiers of what open source AI can do,” said Sridhar Ramaswamy, CEO of Snowflake. “Our research with Arctic will significantly enhance our capability to deliver reliable, efficient AI to our customers.”
The Arctic LLM is part of the Arctic family of AI models, which includes a text-embedding model as well.
Arctic LLM is now available through Hugging Face and will be coming soon to other model gardens like Snowflake Cortex, Amazon Web Services (AWS), Microsoft Azure, NVIDIA API catalog, Lamini, Perplexity, Replicate and Together.
