While hyperscalers and neo-cloud providers may get the lion’s share of attention for providing AI infrastructure, many enterprises are taking a build-it-themselves approach to meet their specific AI requirements. The success of such projects is crucial to achieving business objectives, yet companies face significant challenges as they try to scale pilots to production.
Organizations must keep up with the dynamic, ever-changing demands that AI applications place on compute and network infrastructure, from the data center to the edge. That means architecting systems to grow as demand warrants and to avoid performance bottlenecks. The architecture must also account for AI-driven security vulnerabilities and ensure appropriate defenses are in place.
Yes, it’s a tall order. But here, in simplified form, is a three-step plan for meeting those objectives.
Step one: Go modular
Integrating all the required components in piecemeal fashion for an AI factory is complex, costly, and fraught with integration risk. Start with a modular design, based on proven NVIDIA reference architectures. A modular approach combines pre-validated accelerated computing hardware, AI software, and orchestration platforms, as well as networking and storage capabilities.
A modular strategy speeds implementation and creates a faster time to value for your AI infrastructure. Using modules that combine compute, networking, and storage makes it easier to scale capacity as needed, whether in the data center or at edge facilities.
In addition, the modular approach simplifies the job of addressing varying requirements, from inferencing engines at the edge to massive-scale model training in the data center, while staying within the same solution family.
The same applies to easing integration processes, as modular platforms offer pre-validated software. The Cisco Secure AI Factory with NVIDIA approach, for example, includes hardware (Cisco AI PODS) that is pre-validated to work with NVIDIA AI Enterprise software; Cisco Security and Splunk Observability software; orchestration platforms such as Ubuntu, Red Hat OpenShift, and Rancher by SUSE; as well as storage systems including VAST Data, Everpure (formerly Pure Storage), Hitachi Vantara, Nutanix, and NetApp.
Companies can also choose to manage the hardware and software with the cloud-based Cisco Intersight platform, which provides monitoring and management for physical and virtual infrastructure from the data center to the edge.
Step two: Provide security at every layer
Embedding security throughout your AI infrastructure is critical to ensure continuous monitoring, threat detection, and response. However, this step can introduce tremendous complexity, especially given the bevy of cyber threats that AI introduces. Addressing them means implementing security solutions to cover all components of your AI infrastructure, including AI models, agents, applications, workloads, and the underlying infrastructure.
With agentic AI, which essentially empowers agents with decision-making capabilities, you need to secure agents as if they were employees. That means zero-trust policies should apply, including precise, context-aware controls to enforce least-privilege access for AI agents. If an agent is behaving suspiciously, it should be quarantined and investigated.
A critical benefit of Cisco’s modular approach is having all required security software built in. It simplifies integration and deployment while ensuring all security bases are covered.
Step three: Apply best practices from experts
Even if you follow steps one and two, you may still need assistance in determining your best deployment options.
Working alongside a vendor with a strong partner program and expert guidance can be a great asset. Value-added resellers (VARs) add value through expertise gained from numerous customer deployments and close relationships with their partners. Many also carry relevant certifications, such as the new Cisco AI Infrastructure Specialist Certification, which demonstrates credibility.
Vendors and VARs also offer professional services and NVIDIA enterprise support. The upfront costs are well worth it in the long run to minimize technical deployment and financial risks, lower your overall AI cost per token, and realize faster time-to-value from AI investments.
Learn how the Cisco Secure AI Factory with NVIDIA can help ensure a sound foundation for your enterprise AI projects.
