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Earlier this week, Microchip and Micron unveiled a new PCIe Gen6 AI storage architecture that points to a larger trend across the industry. It goes beyond any single hardware platform. It is about AI infrastructure entering a stage where storage performance matters more than ever before.
Enterprises continue to deploy larger training clusters and expand inference workloads. This means that a key challenge is delivering data quickly enough to keep increasingly powerful accelerators working at full capacity. This is exactly what is putting storage technologies under the spotlight.
“Advancing data center performance is not solved by any single component and our work with Micron highlights the importance of taking a cohesive approach,” said Brian McCarson, corporate vice president and GM of Microchip’s data center solutions business unit.
The goal, according to McCarson, is to combine the companies’ strengths in storage and switching to create AI systems that can handle larger workloads and move data more efficiently.
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It was not long ago that the conversation was almost entirely about securing enough GPUs. Not anymore. Hyperscalers and enterprise IT teams are looking much more closely at the rest of the stack.
Networking, memory and storage are all becoming critical to overall system performance because even the most powerful accelerators deliver less value if they spend time waiting for data. And this is not going to become any easier. Having the fastest chips matters. However, as AI systems continue to scale, the winners may those that can move data through the system most efficiently.
The pressure isn’t coming from one workload. It’s coming from all of them at once. Training still moves enormous amounts of data, but inference is quickly becoming just as demanding. This is partly due to enterprises deploying more AI assistants, coding tools and retrieval-augmented (RAG) applications.
Those systems constantly pull from vector indexes and other datasets. This means storage is spending less time in the background and more time on the critical path.
That’s one reason storage vendors have become much more visible in the AI conversation over the past year. Companies such as VAST Data, Pure Storage and now Microchip and Micron are all trying to solve different parts of the same problem. The GPU may be doing the computation, but the surrounding infrastructure now determines how much of that performance organizations can actually use.
The timing is no coincidence. AI infrastructure spending has accelerated over the past two years. Every new generation of hardware is exposing different bottlenecks. First it was GPU availability. Then it was networking and power. Now storage is joining that list as organizations deploy larger clusters and try to keep expensive AI systems running at high utilization.
These changing dynamics have resulted in new opportunities for vendors that can improve data movement across the entire infrastructure stack.
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Storage vendors are making a similar argument. Rather than focusing on faster SSDs alone, they’re increasingly talking about complete storage architectures that can keep pace with modern AI systems.
“AI infrastructure is entering a new era in which storage performance and ecosystem interoperability must advance in lockstep,” said Larry Hart, senior director of solutions marketing for Micron’s Core Data Center Business Unit.
“Together, the Micron 9650 SSD, the industry’s first mass-produced PCIe Gen 6 SSD, and Microchip’s Switchtec PCIe Gen 6 switching technology demonstrate how a scalable storage architecture can help data centers achieve higher throughput, lower latency and more efficient data movement for next-generation AI, HPC and cloud workloads.”
The emphasis on interoperability is noteworthy. AI infrastructure is becoming too complex for any single component to determine overall performance. Faster GPUs help, but so do faster networks, memory subsystems and storage. If one part of the stack falls behind, it can limit the performance of everything else. That’s why infrastructure vendors are increasingly talking about complete platforms rather than individual products.
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