AI Networking Guide

The questions enterprise architects actually ask before they commit budget to a GPU fabric

Most GPU fabric decisions stall on a handful of recurring questions that vendor decks answer badly. This guide collects the ones we field most often from enterprise IT leaders and network architects, and answers each one directly.

The goal is a vendor-honest reference you can scan quickly, then follow into deeper material when a specific decision needs more depth. Every answer is scoped to the enterprise case: tens to low hundreds of GPUs running mixed training and inference, not the 16,000-GPU hyperscaler builds where different rules apply.

⏱ 18 min read Enterprise-scoped | Vendor-honest | Operations-focused

Key Takeaways

  • Training almost always needs a lossless fabric, while single-request inference often runs fine on well-provisioned lossy networks - large-model inference that shards across GPUs needs lossless treatment like training.
  • For most enterprises, RoCEv2 on Ethernet is the mature choice today, with Ultra Ethernet as the forward path arriving in 2025 and beyond on platforms with committed upgrade paths.
  • Cost-per-port planning misses the real budget impact - optics, cabling, and host adapters often exceed switch port costs, especially at 800G speeds.
  • A dedicated AI fabric typically pays for itself once you cross 16 to 32 GPUs doing distributed training, providing performance and operational isolation from production traffic.
  • Modern lossless Ethernet with RoCEv2 performs within a few points of InfiniBand on real AI benchmarks while avoiding vendor lock-in and reusing existing operational skills.

Operational considerations

Beyond the technical design, enterprise GPU fabrics require clear operational boundaries and ongoing visibility into performance that traditional monitoring does not provide.

Related Resources

FAQs

Frequently Asked Questions

What does the network team own versus facilities in a GPU buildout?

The network team owns the fabric: switches, optics, cabling topology, lossless configuration, and the back-end and front-end network design. Facilities owns power density per rack, cooling capacity including liquid cooling where required, and floor space and weight. The friction shows up at the rack, where GPU servers demand power and cooling far beyond a normal compute rack, so the two teams have to plan rack layout and power budget together before any switch is ordered.

Can I run my GPU traffic and my regular data center traffic on the same switches?

You can, but for distributed training you generally should not, because GPU collective traffic is bursty and lossless-sensitive in ways that conflict with normal production traffic on shared buffers and links. A dedicated back-end fabric for GPU-to-GPU traffic, with the front-end connected to your existing network, is the common and reliable pattern. Small inference deployments are the exception and often coexist fine on the production fabric.

How do I get observability into an AI fabric when problems show up as slow training, not red alarms?

AI fabric problems rarely announce themselves as a down link; they show up as training that is mysteriously slower than the GPUs should allow, so you need telemetry on the fabric itself, not just up-down monitoring. Watch congestion signals, PFC pause frames, ECN marks, buffer occupancy, and per-queue drops, because those are where a lossless fabric quietly degrades. Streaming telemetry from the switches into an observability platform, correlated with job timing, turns a multi-day performance mystery into a same-day fix.

Who should design and operate this if my team has never built a GPU fabric?

Be honest about the gap: a GPU fabric concentrates several specialties - lossless Ethernet tuning, rail-optimized topology, optics selection, and power and cooling coordination - that most enterprise network teams have not had to combine before. The lowest-risk path is to bring in design help for the first build while your team learns the platform, then own day-two operations with the right telemetry in place.

Is Ethernet really competitive with InfiniBand for AI now?

For the enterprise scale this guide addresses, yes. Modern lossless Ethernet with RoCEv2 lands within a few points of InfiniBand on real AI benchmarks while reusing your existing operational skills and avoiding single-vendor lock-in. InfiniBand still leads in the largest latency-critical HPC and frontier-training clusters, so the honest framing is that Ethernet is the better default for most enterprises and InfiniBand earns its place at the extreme end.

Do I have to choose 400G or 800G permanently, or can I change later?

You do not have to choose permanently if you design for the transition. Deploying 400G today on switch silicon with a clear 800G path lets you match current-generation GPUs at proven cost, then step up when NIC bandwidth and collective sizes actually saturate 400G. The mistake to avoid is a topology that forces a full rebuild to move up; a fabric designed for migration treats the speed step as an optics and platform refresh, not a forklift.

Ready to design your enterprise AI fabric?

IVI designs enterprise AI fabrics on Arista platforms with RoCEv2 today and an Ultra Ethernet upgrade path. We pair fabric design with streaming telemetry and observability so performance regressions surface in hours, not days, and hand day-two operational ownership back to your team rather than locking you into the integrator.

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