Cisco has widened its collaboration with NVIDIA to push artificial intelligence beyond centralized data centers, positioning its Secure AI Factory architecture for distributed, real-time deployments. The expansion targets enterprises seeking to operationalize AI closer to where data originates, particularly across environments that demand low latency and continuous decision-making.
The announcement, revealed during NVIDIA GTC and shared by Cisco Chair and CEO Chuck Robbins, signals a shift in how infrastructure vendors approach AI at scale. Rather than anchoring workloads in hyperscale facilities, Cisco is aligning its stack to support inference across edge locations, service provider networks, and enterprise systems.
Robbins said in a LinkedIn post: “At GTC, we announced a major expansion of our Secure AI Factory with NVIDIA helping customer scale AI from the data center to the edge, simply and securely.” He added: “We’re excited about the possibility to help enterprises, neoclouds, sovereign clouds, and service providers move AI from pilot to production without stitching together disconnected systems, so they can deploy faster with security embedded from the start.”
The updated framework introduces infrastructure capabilities that extend across distributed environments such as hospitals, manufacturing facilities, and transportation systems. These locations increasingly require on-site inference, where milliseconds can determine operational outcomes.
NVIDIA Blackwell GPUs power distributed AI inference
Moreover, Cisco is enabling this shift with support for NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs across its UCS and Unified Edge portfolios. This allows enterprises to run AI workloads locally without relying on full-scale data center infrastructure, effectively compressing the distance between compute and data generation.
The company is also rolling out a reference architecture tailored for service providers. By combining Cisco’s networking platforms with NVIDIA’s GPU stack, the design supports managed AI services that can be deployed across customer environments with greater consistency.
However, the move reflects a broader industry transition. AI inference is steadily migrating toward the edge, driven by the need for faster processing, reduced bandwidth costs, and improved data sovereignty. Centralized models alone no longer meet the demands of real-time systems. Cisco has concurrently upgraded its networking layer to support these evolving workloads. New high-speed switching capabilities and deeper integration within its Nexus platform aim to handle the data intensity associated with large-scale AI operations.
Furthermore, the company is addressing one of the most persistent barriers to AI adoption: system complexity. The Secure AI Factory architecture now reduces dependency on fragmented, multi-vendor integrations, enabling organizations to move from pilot programs to production environments with fewer operational bottlenecks.
NVIDIA Founder and CEO Jensen Huang says: “AI factories are transforming every industry, and security must be built into every layer from silicon to software to protect data, applications, and infrastructure.” He adds: “Together, NVIDIA and Cisco are building the secure foundation for AI infrastructure core to edge, so companies can scale intelligence with confidence.”
Security architecture evolves for autonomous AI systems
Security remains central to the expansion. Cisco has introduced extended firewall coverage and new control mechanisms designed to manage AI agents operating across distributed systems. These additions reflect rising concerns around autonomous systems that interact across multiple environments.
In addition, Cisco is integrating its AI Defense capabilities with NVIDIA platforms, enabling monitoring and validation of agent behavior within development and operational workflows. As enterprises deploy more autonomous AI agents, visibility and governance become critical components of infrastructure design.
The updates also respond to emerging risks tied to distributed AI architectures. As models operate across edge, core, and cloud environments, the attack surface expands, requiring coordinated security frameworks that extend beyond traditional perimeters. Ultimately, Cisco’s expanded Secure AI Factory signals a strategic pivot toward making AI infrastructure production-ready. The emphasis now lies on balancing performance, cost efficiency, and security across increasingly decentralized systems, where intelligence must operate continuously and reliably.
