Storage Is the AI Bottleneck. Here's What to Do About It.

Your GPUs are only as fast as the data you can feed them.

There's a persistent misconception in AI infrastructure: that inference is stateless. It isn't. Every large language model processing long-context conversations generates KV cache data that has to live somewhere. When GPU memory fills up, that data spills directly to NVMe storage. If your storage can't keep pace, your model slows down. It's that direct. This is the bottleneck most infrastructure teams aren't talking about — and it's exactly what Graid Technology is solving.

Our SupremeRAID™ solution offloads RAID processing from the CPU to the GPU, delivering: ‍

— Tens of millions of IOPS
— Hundreds of GB/s throughput
— Full enterprise resilience
— without taxing your compute

The result is higher GPU utilization, lower cost per token, and KV cache overflow that hits NVMe at near in-memory speeds.

Watch here as Garrett McKibben and Kelley Osburn break it all down in our latest video. If you're building or evaluating AI infrastructure, this one is worth your time!

And if you're attending NVIDIA GTC next week — come find us. We'll be at Booth 112. Let's talk storage!

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