Front-end vs back-end AI networks: scoping Cisco Nexus fabric for GPU workloads
An AI data center runs two networks, not one. Understanding the front-end and back-end split is the key to sizing the right Cisco Nexus fabric — and not overspending on either.
One of the most useful mental models for an AI data center is that it runs two distinct networks. Get the split right and you size each correctly; conflate them and you either underbuild the part that matters or overspend on the part that does not.
The back-end network: GPU-to-GPU
The back-end is the high-speed, lossless fabric that connects GPUs to each other for training. It carries all-to-all, synchronized traffic and is the most demanding network in the building — typically 400G or 800G per port, non-blocking, with RoCEv2 and careful congestion control. This is where Silicon One and the latest Nexus switches earn their place.
The front-end network: everything else
The front-end is the more familiar network: storage access, data ingestion, management, user and API access, and north-south connectivity to the rest of the enterprise. It is important, but it does not need the extreme bandwidth and lossless tuning of the back-end — so sizing it like the back-end wastes money.
How to scope each
- Back-end: ports per GPU, non-blocking spine/leaf ratio, optics and fiber per GPU port, congestion strategy.
- Front-end: storage throughput, ingestion bandwidth, management and out-of-band, north-south uplinks.
- Shared: power and cooling per rack, observability across both, and segmentation between them.
“Two networks, two budgets. The back-end is where you spend for performance; the front-end is where you spend for reach.”
— Uniqcli data center practice
Frequently asked questions
Can one switch serve both front-end and back-end?
Different roles usually call for different port speeds and configurations, but the Nexus 9000 family covers both, so you stay in one operational model. We map models to each role in the quote.
What speed does the back-end need?
Training clusters commonly run 400G or 800G per GPU port in a non-blocking design. The exact figure comes from GPU count and model size.
Do we need a separate management network?
Yes — out-of-band management and observability are part of the front-end design and are worth scoping explicitly so the fabric is operable from day one.
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.
