Snowflake deepens NVIDIA tie-up to accelerate ML inside its AI Data Cloud

What happened

Snowflake announced a native integration of NVIDIA's CUDA-X libraries into Snowflake ML, allowing enterprises to run GPU-accelerated machine learning workflows directly on the Snowflake platform. This move strengthens Snowflake's AI Data Cloud positioning by tightening the link between governed enterprise data and high-performance model training and inference, a key strategic pillar already highlighted in its Cortex and Arctic initiatives. By bringing popular NVIDIA data science libraries preinstalled, Snowflake reduces friction for data scientists and may increase compute-intensive consumption, which is the primary driver of its revenue model. The integration also helps Snowflake stay competitive versus Databricks and hyperscaler-native warehouses, which have been leaning heavily into AI and GPU capabilities. While the announcement is product- and ecosystem-focused rather than financial, it incrementally supports the long-term growth narrative that AI services can sustain high-20s to low-30s product revenue growth if customer adoption follows.

Implication

For investors, this announcement is a positive incremental signal that Snowflake is executing on its AI roadmap by embedding best-in-class NVIDIA GPU libraries directly into its core platform. If customers adopt these capabilities at scale, it could increase compute consumption per workload, supporting the thesis that AI can drive durable product revenue growth and reinforce Snowflake’s data gravity. The move also reinforces Snowflake’s ecosystem narrative versus Databricks and hyperscalers by offering a more integrated experience for ML practitioners within the governed data plane, which may help in competitive bake-offs. However, the news does not yet provide evidence of actual AI-driven usage uplift, nor does it change the reality that the stock still trades at a rich multiple with exposure to consumption variability and intense competition. As a result, the integration is best seen as a strategic building block that tilts the risk-reward slightly more favorably over the long term, but not enough on its own to warrant a shift from a HOLD/NEUTRAL rating pending clearer adoption and financial impact data.

Thesis delta

The prior thesis was HOLD/NEUTRAL: a strong, differentiated data platform with robust expansion metrics and a credible AI roadmap, but priced for a high level of execution amid competitive and consumption risks. This NVIDIA CUDA-X integration modestly strengthens the AI execution leg of that thesis by making it easier for Snowflake customers to run GPU-accelerated ML directly where their data resides, improving the odds that AI features translate into incremental consumption over time. Nonetheless, the change is incremental rather than transformational, so our stance remains HOLD/NEUTRAL with a slightly more constructive bias contingent on future proof of AI-driven usage and revenue uplift.