Cisco AI PODs deployment services, from design to a validated pod.
Cisco AI PODs are modular, pre-validated infrastructure for enterprise AI: UCS compute with NVIDIA or AMD GPUs, a Nexus fabric, Intersight lifecycle management, and the software and security layers on top. Ngenium’s senior engineers size, deploy, integrate and validate them in your environment, so the pod you procure performs the way its design says it should. Services-only: you buy the hardware and software through your Cisco partner or distributor; we engineer it.
Everything between a delivered rack and a pod your team can use.
Pre-validated does not mean pre-integrated into your environment. Your network, identity, storage, security and operations all have to meet the pod, and the work that decides whether it performs on day one is engineering, not logistics.
Sized to the models you will actually run.
Inference, RAG, fine-tuning or training: each needs a different mix of GPUs, memory, storage throughput and fabric bandwidth. We map your workloads to the right pod type and scale, and agree segmentation and security requirements before anything is ordered.
UCS GPU servers, brought up and aligned.
Cisco UCS GPU servers with NVIDIA or AMD accelerators, deployed and managed through Cisco Intersight, with firmware, drivers and the GPU runtime validated before workloads arrive.
A lossless Nexus fabric for GPU traffic.
Nexus 9000 leaf-and-spine at 400G or 800G, configured for RoCEv2 and lossless operation, tested under load, and managed through Nexus Dashboard.
Your storage platform, connected for AI throughput.
Integration with the storage platform in the design — Cisco lists VAST Data, NetApp, Pure Storage and Nutanix — tested at the data rates training and inference actually demand.
Kubernetes, NVIDIA AI Enterprise and Cisco AI security.
Red Hat OpenShift or the platform your design calls for, NVIDIA AI Enterprise, and Cisco AI Defense, Hypershield or Isovalent where they are part of the design — applied with the AI Security and Safety Framework (SAIF).
Proven under load, then handed over.
Performance validated against agreed targets, observability wired to your operations, runbooks and knowledge transfer — or a hand-off to Port53 for 24x7 managed security operations.
Securing the AI that runs on it? See our Cisco AI Defense implementation services →
Assess, architect, deliver, operate: one accountable team.
Cisco’s own AI PODs documentation says services from Cisco and its certified partners accelerate deployment. That is the work Ngenium does, as services only. After go-live, the pod can move into managed security operations through our strategic partnership with Port53.
UCS, NVIDIA GPUs and the full stack, working within days.
At a major U.S. healthcare provider, a multi-vendor Secure AI Factory deployment had stalled for months. Ngenium engineers delivered the full stack — UCS with NVIDIA GPUs, OpenShift, Isovalent, AI Defense and Splunk — working and observable within days.
Why teams bring in Ngenium.
- Senior engineers only — the engineer who sizes your pod is the one who deploys it.
- Services-only — we never resell hardware or software and carry no product margin; you procure through your Cisco partner or distributor, and we engineer it.
- Cisco depth, multi-vendor fluency — UCS, Nexus, Intersight and AI Defense alongside NVIDIA, AMD, Red Hat, Nutanix and Splunk.
- Regional reach — senior engineers across North America, Europe and Latin America.
About Cisco AI PODs deployment.
What is a Cisco AI POD?
Cisco describes AI PODs as modular, pre-validated AI infrastructure and the foundation of the Cisco Secure AI Factory: Cisco UCS compute with GPUs, Nexus networking and partner storage, managed through Cisco Intersight and Nexus Dashboard. They are designed with NVIDIA and validated through Cisco Validated Designs.
Do you sell Cisco AI PODs?
No. Ngenium is services-only. You buy the hardware and software through your Cisco partner or distributor; Ngenium sizes, deploys, integrates and validates it. That keeps our advice tied to your architecture rather than a product margin.
Which workloads and GPUs does it cover?
Cisco positions AI PODs for inference, fine-tuning and training, with one pod type aimed at inference and edge and another at training and fine-tuning. Cisco lists both NVIDIA and AMD GPU options, and our engineers work with both. We size the pod to the models you actually run.
How long does a deployment take, and who runs it afterwards?
It depends on scale, site readiness and integration scope. In one delivered engagement, a stalled Secure AI Factory deployment reached a validated, observable state within days. A 30-minute scoping call gives you an honest estimate. After go-live your team can run the pod with our support, or Port53 can provide 24x7 managed security operations.
Do you deploy AI PODs for a Cisco partner’s customers?
Yes. Cisco channel partners bring Ngenium in to deliver the engineering for their customers, including white-label engagements under NDA.
Planning a Cisco AI POD?
Talk to the senior engineers who would size and deploy it — not a salesperson.