Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer

Pinterest optimized ML serving network efficiency by implementing Feature Trimmer to reduce bandwidth bottleneck.

• •Network bandwidth became the limiting factor in root-leaf architecture instead of GPU compute capacity
• •LZ4 compression reduced bandwidth 20% with 5% CPU overhead and 5ms latency trade-off
• •Feature Trimmer implements "Send What You Use" approach to trim unused features, targeting ~50% network reduction
• •Model signatures exported as module_info.json define required features per model
• •Root and leaf maintain synchronization through bundle artifacts and staged deployment semantics

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