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Nvidia is expanding the role of its Data Processing Units (DPUs) from simple infrastructure offloaders to active security controllers for agentic AI workloads. By enforcing boundaries on agent actions and data access independently of the agents themselves, this "scale-in" strategy aims to secure the broader AI factory as autonomous systems become more prevalent. This shift addresses critical safety concerns in multi-agent environments while deepening Nvidia's position as a central provider of enterprise AI infrastructure.
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Nvidia’s scale-in play: Controlling agents is the next infrastructure priority

SiliconANGLE21 min read

Nvidia Corp. is extending the data processing unit from infrastructure offload to a broader security role across the artificial intelligence factory. The opportunity is to make agentic AI safer to operate at scale. That’s according to Gilad Shainer, Nvidia’s senior vice president of networking, who sat down with theCUBE last week at our NYSE studios. […] The post Nvidia’s scale-in play: Controlling agents is the next infrastructure priority appeared first on SiliconANGLE.

Nvidia Corp. is extending the data processing unit from infrastructure offload to a broader security role across the artificial intelligence factory. The opportunity is to make agentic AI safer to operate at scale. That’s according to Gilad Shainer, Nvidia’s senior vice president of networking, who sat down with theCUBE last week at our NYSE studios. The strategic implication is an even larger role for Nvidia in enterprise infrastructure. With the current climate focused intensely on AI safety, this accelerates trust. It also deepens Nvidia’s foothold on the ecosystem. Nvidia is at it again. Having built out its scale-up, scale-out and scale-across network architecture, the company is introducing another category it is calling scale-in. Its purpose is to connect more of the AI factory’s resources while extending security controls across the infrastructure. In Nvidia positioning, the DPU extends beyond a device that secures access to a server and becomes a separate place to monitor and control activity across the factory. The reason for this move is an AI system that simply answers a question presents one set of infrastructure requirements. A system in which agents make repeated model calls, retrieve information, access applications and then act, presents a more pressing challenge.…

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