#TechDeveloperConfluence2025 | Breaking Bottlenecks in AI

At #TechDeveloperConfluence 2025, Jayapaul P, Lead Architect at Pure Storage, during the session ‘Breaking Bottlenecks in AI: RDMA, KV Cache, and Direct Data Paths at Scale,’ discussed how optimizing AI inference engines with KV caches can improve efficiency by reusing context and reducing unnecessary calculations.

Jayapaul also highlighted how offloading data to SSD and remote storage helps manage memory and storage costs, while RDMA and GPU direct storage technologies reduce CPU interference, boosting performance. He wrapped up by emphasizing the role of exascale architecture in achieving high throughput, low latency, and cost savings in AI development.

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