This post was originally published on Pure Storage
Today’s AI models demand not only more data but also fundamentally different approaches to how that data is stored and accessed. The age of text-only data is now a thing of the past—audio and video are becoming mainstream data types. Because of the rise of multimodal data, object storage is gaining momentum.
Object storage opens the door for both flexibility and scale. With a flat namespace and rich metadata capabilities, object storage allows organizations to store, manage, and analyze data at scale.
In AI/ML environments, storage performance (throughput and latency) is critical for an organization to stay not only competitive but ahead of the pack. Any sort of bottleneck can cost organizations millions in GPU compute time or delay critical model deployments.
AI training workflows require extremely large and continuous data movement from storage systems to GPUs. When these expensive computational engines aren’t fed fast enough, they sit idle, turning million-dollar investments into costly underutilized assets while delaying AI innovation timelines.
This bottleneck is where Remote Direct Memory Access (RDMA) technology becomes game-changing, as it creates direct pathways between storage and GPU memory. We’re excited to announce that Pure Storage® FlashBlade® Object Store will soon support S3 over RDMA specifically
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