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Explore Google Cloud Storage's Bet on Becoming AI's Critical Infrastructure | AppDevANGLE with 7,114 views, 91 likes, and 0 comments. Experience the impact of this video content that has captured audience attention.

Enterprises bought the GPUs. Now the storage layer is limiting what those accelerators can actually do. In this conversation, analyst Paul Nashawaty speaks with Ronen Schwartz, VP of Products for Google Cloud Storage at Google, about a bottleneck that rarely surfaces in procurement conversations but consistently appears in production: the data delivery path feeding accelerators can't keep pace with the compute it's supposed to serve. The conversation moves from training and inference through to agentic architectures, covering the engineering decisions Google made to bring internal infrastructure capabilities — built over years of running frontier AI workloads — into enterprise cloud environments. For buyers and architects evaluating AI storage, the specifics matter: performance claims vary widely across vendors, and the architectural differences between zonal colocation, managed Lustre, and standard object storage translate directly into measurable differences in accelerator utilization and operational cost. What we get into: - How GPU utilization and training economics degrade when storage throughput and checkpoint performance are treated as secondary infrastructure concerns - Why training, inference, and agentic pipelines impose fundamentally different storage requirements, and the cost of applying a single-tier architecture across all three - What enterprises should evaluate when comparing AI storage options, including throughput ceilings, latency characteristics, and checkpoint recovery speed - How zonal colocation addresses the data proximity problem that multi-region storage architectures create for accelerator-bound workloads - What agent-ready storage infrastructure requires beyond low latency — shared data access, cost management at scale, and state persistence across active and hibernated agent instances - Where Google Cloud Storage's roadmap is focused, including inference optimization, storage automation, and expanding support for HPC and EDA workloads moving to the cloud Learn more about Google Cloud’s storage innovations here :https://cloud.google.com/blog/products/storage-data-transfer/next26-storage-announcements?e=48754805 Try Cloud Storage for free: https://cloud.google.com/storage Follow Ronen Schwartz on LinkedIn: https://www.linkedin.com/in/ronenschwartz/

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