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GPU memory is the most expensive resource in production AI, and it's also the one running out fastest.
Long context windows and multi-turn conversations force AI models to repeatedly recompute information they've already processed, consuming GPU memory and compute that could otherwise serve additional users or generate new responses.
Instead of treating GPU memory as the limiting resource, why not extend it with much cheaper storage technologies?
Weka, for one, believes that cheap flash storage can close that gap. The company's NeuralMesh 6 software platform, launching alongside its first self-designed hardware line, Wekapod 3, extends what Weka calls Augmented Memory Grid, an approach that aggregates NAND flash to behave like GPU memory at a fraction of the cost.
This is an active and increasingly crowded category. Dell, NetApp, Pure Storage and VAST have all repositioned toward AI infrastructure over the past two years and Weka is one of several vendors arguing it's built for this specific moment rather than adapting to it.
"What we're seeing now with customers is they're chasing availability of compute, and once they get new allocation from anyone, they want to be able to grab it and start running right away," Weka co-founder and CEO Liran Zvibel, told VentureBeat.
The potential payoff is straightforward: better utilization of existing GPU investments, lower inference costs and faster deployment of new AI workloads without waiting months for additional GPU capacity.
The technology is most relevant for organizations already operating AI at scale or expecting rapid growth in usage, particularly enterprises building internal copilots, customer service agents, software engineering assistants or retrieval systems with long context windows. Smaller deployments may see less immediate benefit than organizations where GPU utilization has already become a limiting factor.
NeuralMesh 6 adds four capabilities aimed directly at a functionality gap Zvibel says has been costing Weka deals in competitive evaluations.