AI inference gets a new tier as context windows grow
SiliconANGLE Victoria Gayton
AI agents are pushing storage into the inference stack. That means more data has to move fast, not just more GPUs.
Based on reporting by SiliconANGLE, Victoria Gayton — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
AI infrastructure planning is shifting. The old obsession was training big models; now the pressure is moving to inference, where agentic systems reason, act, and then reassess what they just did. That creates longer context windows, bigger KV caches, and a lot more data that has to be available immediately if the app is going to feel responsive.
Scott Shadley of Solidigm, Anat Heilper of Vast Data, and Ben Lee of Supermicro made the case that storage is no longer a backroom concern. It sits in the path of the request itself. Shadley said the new reality is less about one metric and more about the way data grows in size, importance, and movement through the stack. In plain terms: the bit of data matters, but so does how quickly it gets from one place to another.
That is why SSDs are getting a new role. As context windows grow, GPU memory can’t keep everything an agentic workload needs during inference. Shadley described a middle layer that sits below memory and above network storage, enabled by NVMe SSDs. Solidigm’s drives feed that tier, Supermicro builds them into rack-scale systems, and Vast Data’s AI Operating System handles the network storage and data services around the larger workload.
Heilper said KV cache can effectively replace some compute with storage, which matters because GPUs are expensive. She pointed to work with Nvidia Dynamo, saying it produced 20 times faster time-to-first-token and 90% savings in GPU time. Those gains depend on the storage stack being tuned to the workload, not just bolted on after the fact.
Lee’s view was simpler: no single setup fits everyone. Supermicro’s CMX proposal is aimed at large AI clusters with heavy data demands, but other deployments will need different mixes of memory, local SSDs, and network storage. The bigger point is that this is turning into a whole-rack problem, and the vendors involved know it.
My take — AI-written commentary, not fact-checked reporting
The interesting part here is not that storage matters; it’s that AI vendors spent years acting like it didn’t. Now the bill for all that GPU worship is arriving, and it looks annoyingly physical: SSDs, racks, latency, the usual boring stuff that wins. Europe should enjoy this moment too, because infrastructure is where the bragging stops and the engineering starts.
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