Snowflake to burn $6B on AWS Graviton CPUs and AI accelerators

Summary

off-Prem Snowflake to burn $6B on AWS Graviton CPUs and AI accelerators Dataware house gambles cloud conveniences, AI accelerated insights will justify the cost. Cloud data warehouse Snowflake plans to spend $6 billion on Amazon’s custom Graviton CPUs and AI accelerators over the next five years.The collab aims to reduce friction in connecting Snowflake customer data with a growing number of AI services built atop AWS’ cloud infrastructure.“We are making it easier for enterprises to bring AI directly to governed data, so they can move faster, operate with greater density and create measurable impact at scale,” Snowflake CEO Sridhar Ramaswamy said in a canned statement. Snowflake is a long-time AWS customer, having built the company atop the cloud titan's servers going back to 2011. Over the past few years, Snowflake has shifted an increasing amount of compute from Intel and AMD CPUs to Amazon’s own Arm-based Graviton instances. Now in their fifth generation, Amazon’s latest Graviton processors cram 192 Arm Neoverse V3 cores which are fed by 12 channels of memory up to 8800 MT/s.As we’ve previously reported, CPUs are back in the spotlight again after years of being overshadowed by GPUs and other AI accelerators.The models themselves still run on GPUs, but the tools and functions those models call — a SQL query or Python script, for example — do not. Those workloads still rely on CPUs.This has driven renewed demand for CPU cores as each agent’s performance is inherently limited by how quickly the processor can service the request.Under the agreement, Snowflake will run and train its GenAI models and services using a combination of GPUs running in AWS and Graviton CPU cores. For example, Snowflake says that its Cortex AI platform can convert natural language to SQL queries, summarize data, and conduct sentiment analysis.According to Amazon, Snowflake’s lifetime AWS marketplace sales crossed $7 billion and exceeded $2 billion during the 2025 calendar year. Clearly the d...