AI-ready data centers for government: sizing the GPU network with Cisco Silicon One and Nexus
Standing up AI in an agency data center is a networking problem before it is a GPU problem. Here is how to size the fabric — Silicon One, Nexus 9000, optics, and lossless Ethernet — for AI workloads that have to stay on-premises.
An AI data center is a networking project before it is a GPU project. GPUs are the most expensive line item in the build, and an idle GPU is wasted budget — so the network that feeds them decides whether the investment pays off. For agencies that cannot send sensitive data to a public AI cloud, that fabric is now theirs to design.
The GPU is only as fast as the network behind it
In an AI training cluster, a single job synchronizes gradients across hundreds or thousands of GPUs many times per second. Every GPU waits on the slowest flow in that exchange, so a lossy or oversubscribed network stalls the whole cluster. The back-end fabric — the GPU-to-GPU, east-west network — is where utilization is won or lost, and it is a different design problem from the campus or the traditional data center.
What Cisco is building for the AI era
- Silicon One G300 — 102.4 Tbps switching silicon designed for energy-efficient AI infrastructure, built for the scale of large GPU networks.
- Nexus 9000 (9300/9400) — switches purpose-built for both front-end and back-end AI networks.
- Nexus One — unified, cloud-native operations across traditional and AI workloads.
- Lossless Ethernet (RoCEv2 with PFC and ECN) — the open, multi-vendor alternative to proprietary InfiniBand fabrics.
How to size the fabric
- GPUs per rack, and the power and cooling each rack can actually deliver.
- Back-end ports per GPU — often 1:1 at 400G or 800G for training clusters.
- A non-blocking spine/leaf ratio so no link is the bottleneck under all-to-all traffic.
- Optics and fiber counted per GPU port and uplink, not estimated.
- A separate front-end network for storage, management, and north-south access.
“Size the back-end fabric for the workload, not the switch you already know — an AI cluster fails at the slowest flow, not the average one.”
— Uniqcli data center practice
Uniqcli scopes the GPU fabric, optics, power, and services as one TAA-compliant estimate, then validates the final Cisco bill of materials before signature.
Frequently asked questions
Do we need InfiniBand for AI workloads?
Not necessarily. Lossless Ethernet (RoCEv2) on Cisco Nexus delivers the low-latency, lossless behavior AI training needs, with the operational familiarity, security tooling, and multi-vendor openness of Ethernet.
Can AI training and inference stay fully on-premises?
Yes. With the right GPU servers and a non-blocking Nexus fabric, both run in your facility — often a hard requirement for classified, CUI, or otherwise regulated data.
Is the AI hardware TAA-compliant for federal buyers?
We quote TAA-compliant Cisco switching and optics with country-of-origin documentation per line item. See our guide to TAA-compliant AI procurement.
Uniqcli Team
The Uniqcli Team is an authorized Cisco partner specializing in Catalyst wireless, switching, datacenter fabric, licensing, and managed services for U.S. federal, state, local, and education customers. We scope Cisco bills of materials, validate procurement paths (TAA, FIPS, contract vehicles), and deliver design, deployment, and managed operations.
