Pinterest Engineering Blog

PinterestJun 25, 2026

Achieving Near-Linear Training Scalability for Pinterest’s Foundation Models

Here's a 3-sentence summary of the blog post on achieving near-linear training scalability for Pinterest's foundation models: To achieve near-linear training scalability for Pinterest's Foundation Models, Pinterest Engineering team optimized distributed training by reducing the volume and cost of distributed embedding communication, with a focus on communication bandwidth. They implemented quantized communications (QComms) to compress embedding tensors, balanced sharding to ensure an even distribution of embedding workload among GPUs, and bandwidth-aware embedding optimization to reshape the payload itself, leading to near-linear scaling across multiple nodes. These optimizations enabled the team to unlock larger models that drove significant engagement gains across Pinterest's recommendation surfaces, with a 7.5x scaling factor achieved on 8 nodes.

Machine LearningData

12 min

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Pinterest Engineering Blog

Latest Articles

From Activity to Intent: Generating User Journeys with LLMs

Metrics Board: Building an Agent-ready Metrics Layer

Partition Finalization in Pinterest’s Next-Generation DB Ingestion Framework

Beyond Two Towers: Launching the 3-Tower Engagement Co-Train Model (Part 2)

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Securing Infrastructure at Scale: Introducing Pinterest’s Resource Provisioner Pipeline (RPP)

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