Snap Cuts Data Processing Costs 76 Percent With GPUs

Snap moved its A/B testing pipeline from CPU-only Spark workloads to NVIDIA cuDF on Google Cloud L4 GPUs and saw a 4x runtime speedup with 76% daily cost savings. The scale is significant - Snap processes over 10 petabytes of data in a three-hour morning window, running thousands of experiments across 6,000 metrics for its 940 million monthly active users. They needed only 2,100 concurrent GPUs instead of the projected 5,500. The move is a case study for NVIDIA's push to make GPU-accelerated data processing a default rather than a specialty - Databricks, Snowflake, and Google BigQuery have all been adding GPU compute options in the same direction. For Snap specifically, cheaper and faster experimentation means more feature iterations shipped per quarter, which is the kind of compounding advantage that's hard to see from the outside.