AI is not just eating compute budgets, it is punishing data center networks.
Once you move from a single GPU box to real distributed training, the bottleneck usually stops being “more GPUs” and starts being “can the fabric keep up?” That is why 2025 looks like a three-way fight in the AI data center :
- Ethernet (with RoCE and 400G/800G/1.6T links)
- InfiniBand (the long-time high-performance favorite)
- Omni-Path (re-emerging as a serious AI/HPC challenger)
In this guide, Sky High Supply breaks down what is actually different about AI networking and compares Ethernet, InfiniBand, and Omni-Path in practical, buyer-friendly terms. If you are planning GPU clusters, AI labs, or “Phase 1” AI data center upgrades, this is the fabric conversation you need to have before you shop for hardware.
Why AI Data Center Networks Are Different
Traditional enterprise networks were built around:
- North-south traffic: clients hitting apps, apps hitting databases
- Mostly TCP traffic: packet loss is annoying but not catastrophic
- Milliseconds of tolerance: acceptable for web and business workloads
AI training flips that model.
Two networks, one data center
Modern AI data centers increasingly split into:
- Frontend networkConnects users, APIs, and services to the cluster Runs “normal” TCP/IP traffic Looks like a high-end cloud data center
- Connects users, APIs, and services to the cluster
- Runs “normal” TCP/IP traffic
- Looks like a high-end cloud data center
- Backend / AI fabricConnects GPUs to GPUs and GPUs to storage at massive scale Runs collective operations (all-reduce, all-gather, broadcast) that synchronize model parameters Demands ultra-low latency, high bandwidth, and very predictable performance
- Connects GPUs to GPUs and GPUs to storage at massive scale
- Runs collective operations (all-reduce, all-gather, broadcast) that synchronize model parameters
- Demands ultra-low latency, high bandwidth, and very predictable performance
If the backend fabric is slow or congested, GPUs sit idle and your extremely expensive cluster behaves like a mid-range lab box.
East-west traffic and RoCE
Training large models generates enormous east-west traffic inside the data center. Instead of a few big flows, you get thousands of parallel, latency-sensitive flows moving in bursts.
To cope with that, most AI fabrics rely on:
- RDMA (Remote Direct Memory Access) – move data between hosts without waking CPUs
- RoCEv2 (RDMA over Converged Ethernet) – RDMA on top of Ethernet, heavily used by hyperscalers
- Aggressive congestion control and often Priority Flow Control (PFC) to keep the fabric effectively lossless
This is the context where Ethernet, InfiniBand, and Omni-Path compete.
The Three Contenders at a Glance
In 2025, buyers essentially have three serious options for AI back-end fabrics:
Ethernet
- Speeds: 400G and 800G today; 1.6T on the roadmap
- Transport: RoCEv2 for AI workloads
- Strengths: Ubiquity and vendor diversity Strong price/performance Operational familiarity for most network teams
- Ubiquity and vendor diversity
- Strong price/performance
- Operational familiarity for most network teams
- Weaknesses: Achieving near-lossless, deterministic performance at multi-thousand GPU scale is non-trivial Requires careful tuning of congestion control and QoS
- Achieving near-lossless, deterministic performance at multi-thousand GPU scale is non-trivial
- Requires careful tuning of congestion control and QoS
Product tie-in: Browse our curated 400G & 800G Transceivers and High-Density Fiber Cable Assemblies designed for RoCE-based AI fabrics.
InfiniBand
- Speeds: HDR, NDR, and newer generations closely tied to GPU roadmaps
- Transport: Native RDMA with a mature HPC stack
- Strengths: Ultra-low latency and jitter Proven at the very largest AI/HPC scales Tight integration with NVIDIA systems and software
- Ultra-low latency and jitter
- Proven at the very largest AI/HPC scales
- Tight integration with NVIDIA systems and software
- Weaknesses: Vendor lock-in Higher cost and complexity Requires specialized skills and tooling
- Vendor lock-in
- Higher cost and complexity
- Requires specialized skills and tooling
Omni-Path
- Speeds: 400G with the latest generation (CN5000 family)
- Transport: Custom, lossless fabric optimized for collectives
- Strengths: Very strong collective communication performance for AI/HPC Designed to scale large GPU clusters with predictable behavior Positioned as an alternative to both RoCE and InfiniBand lock-in
- Very strong collective communication performance for AI/HPC
- Designed to scale large GPU clusters with predictable behavior
- Positioned as an alternative to both RoCE and InfiniBand lock-in
- Weaknesses: Smaller ecosystem and fewer reference deployments Perceived risk from prior generations and narrower talent pool
- Smaller ecosystem and fewer reference deployments
- Perceived risk from prior generations and narrower talent pool
Ethernet for AI: The Default Contender
Ethernet is still the default for most data centers, and the industry is working hard to keep it that way for AI.
Why Ethernet keeps winning mindshare
-
Clear roadmap
400G and 800G Ethernet are shipping, with 1.6T firmly on the horizon. That roadmap is driven explicitly by AI and ML workloads. -
Hyperscaler proof points
Large cloud providers have publicly documented massive RoCEv2-based AI networks, using carefully engineered spine-leaf fabrics and congestion control. -
Vendor diversity
Multiple switch, NIC, and optical vendors actively compete in AI-optimized Ethernet designs. That means more options, better pricing, and less risk of single-vendor lock-in. -
Operational familiarity
Most network teams already know Ethernet deeply. Extending those skills into AI fabrics is an evolution, not a reinvention.
Strengths of Ethernet for AI fabrics
- Cost and availability – Easy to source optics, switches, NICs, and cabling from multiple vendors
- Convergence – Ability to run “normal” data center traffic and AI traffic on the same physical fabric (when designed correctly)
- Tooling – Mature monitoring and automation ecosystems
Where Ethernet bites you
- Lossless requirements – RoCE likes an effectively lossless network. Misconfiguring PFC or congestion control can cause head-of-line blocking or congestion collapse.
- Very large-scale collectives – At tens of thousands of GPUs, Ethernet congestion management becomes more challenging than on some specialized fabrics.
Who should lean Ethernet?
- Enterprises and service providers who: Want to stay on open standards Already run large-scale Ethernet fabrics Plan to grow AI capacity in phases instead of jumping directly to hyperscaler-style clusters
- Want to stay on open standards
- Already run large-scale Ethernet fabrics
- Plan to grow AI capacity in phases instead of jumping directly to hyperscaler-style clusters
For pods of a few hundred GPUs and incremental growth, a well-designed RoCE Ethernet fabric is usually the most rational first step.
InfiniBand: NVIDIA’s Low-Latency Powerhouse
InfiniBand is the traditional high-performance interconnect for HPC and is now deeply woven into many high-end AI systems.
Why InfiniBand dominates at the top end
- Latency and jitter – Extremely low and predictable, ideal for large collective operations
- Tight GPU integration – Many high-end GPU systems and reference designs assume InfiniBand in the fabric
- Mature software stack – Deep integration with MPI, collective libraries, and performance tools
Trade-offs with InfiniBand
- Vendor lock-in – You are effectively tying your networking future to a single primary vendor
- Specialized skills – InfiniBand requires its own tooling and operational expertise
- CapEx and OpEx – Higher hardware and support costs, plus a steeper learning curve
Who should lean InfiniBand?
- Organizations running very large, tightly-coupled training clusters with aggressive SLAs
- Teams already invested in HPC workflows and GPU-dense supercomputing
- Research institutions and hyperscalers chasing peak performance and benchmark leadership
Omni-Path: The Comeback Challenger
Omni-Path started inside Intel, went quiet, and is now back under Cornelis Networks with a very specific mission: be the fabric of choice for AI and HPC collective communication without locking you into an InfiniBand-style ecosystem.
What is different now
The latest Omni-Path generation:
- Targets 400G fabrics with strong collective performance
- Emphasizes lossless, congestion-free behavior at large scale
- Positions itself as a high-performance alternative to RoCE Ethernet and a less restrictive alternative to InfiniBand
The catch
- Ecosystem size – Fewer operators, integrators, and reference designs
- Perceived risk – Buyers remember the first Omni-Path chapter and want roadmap clarity
- Talent pool – Smaller population of engineers with hands-on experience at scale
Who should consider Omni-Path?
- AI/HPC shops willing to be early adopters for better collective performance and ROI
- Organizations skeptical that RoCE Ethernet will scale efficiently for their workloads
- Teams that want high performance but dislike the idea of pure InfiniBand lock-in
How to Choose: A Practical Decision Framework
You do not need marketing slides. You need a clear filter.
1. Planned scale in the next 3–5 years
- Up to ~256 GPUs (a few racks)Well-designed RoCE Ethernet is usually enough and easiest to justify.
- Well-designed RoCE Ethernet is usually enough and easiest to justify.
- 256–2,000 GPUsCompare InfiniBand vs. RoCE Ethernet, and optionally Omni-Path for performance-sensitive workloads.
- Compare InfiniBand vs. RoCE Ethernet, and optionally Omni-Path for performance-sensitive workloads.
- 2,000+ GPUs, tightly coupled trainingInfiniBand or Omni-Path should be on the table. Ethernet can work, but only with extremely careful design and top-tier silicon.
- InfiniBand or Omni-Path should be on the table. Ethernet can work, but only with extremely careful design and top-tier silicon.
2. Risk tolerance for vendor lock-in
- Low tolerance: favor Ethernet (RoCE) for open standards and vendor diversity
- Medium tolerance: evaluate Omni-Path as a middle ground
- High tolerance: InfiniBand is acceptable if you are already all-in on a single ecosystem
3. Team capabilities
Be brutally honest about skills:
- Does your team know how to design and operate a lossless RoCE fabric?
- Do you have the budget and appetite to hire InfiniBand/Omni-Path expertise?
- Are you comfortable being a reference customer for newer technology?
4. Performance vs. ROI
- If you are chasing headline benchmark numbers, InfiniBand or Omni-Path may be worth the premium.
- If your CFO cares about cost per token trained or per experiment completed, RoCE Ethernet can offer a more balanced performance-to-cost ratio.
Hardware Implications: What This Means for Your Shopping List
For Sky High Supply customers, this is where architecture turns into carts and POs.
1. Optics and Cables
- 400G/800G transceivers and DAC/AOC cables tuned for AI data center use
- High-density MTP/MPO trunk cables for spine-leaf designs
- Short-reach DACs for top-of-rack and leaf switch connections
Explore: 400G & 800G Optics and DAC/AOC Assemblies.
2. Structured Cabling and Panels
- Fiber patch panels sized for AI pod growth
- Pre-terminated trunk assemblies to simplify large-scale deployments
- Labeling and color-coding strategies that align with your AI pod or cluster layout
See: Fiber Patch Panels & Enclosures and Pre-Terminated Fiber Trunks.
3. Power and Rack Infrastructure
AI fabrics come with high-density racks and aggressive power demands:
- PDUs sized for GPU-dense racks
- Cable management systems that keep high-count fiber runs clean and maintainable
- Proper separation between power and signaling to reduce noise and service headaches
Visit: Rack & Power Accessories.
4. Staged “AI Pod” Bundles
Consider staging your build-out around AI pods:
- Define a standard rack or pod (e.g., 32–64 GPUs)
- Align optics, cabling, and panels to that pod size
- Repeat the pattern as you scale
Checklist: Questions to Ask Vendors and Integrators
When you talk to hardware vendors, cloud providers, or integrators, use questions that expose real-world trade-offs:
- Topology & scaleWhat spine-leaf topology do you recommend for our GPU count and 3-year growth plan? How do we expand without ripping up the entire fabric?
- What spine-leaf topology do you recommend for our GPU count and 3-year growth plan?
- How do we expand without ripping up the entire fabric?
- Congestion controlFor Ethernet: Which congestion mechanisms (PFC, ECN, DCQCN, etc.) are validated for RoCE at our scale? For InfiniBand/Omni-Path: How do you handle incast and hot-spots during collectives?
- For Ethernet: Which congestion mechanisms (PFC, ECN, DCQCN, etc.) are validated for RoCE at our scale?
- For InfiniBand/Omni-Path: How do you handle incast and hot-spots during collectives?
- Telemetry & troubleshootingHow do we see queue depths, drops, and congestion events in real time? Which tools do you recommend for root-cause analysis when training slows down?
- How do we see queue depths, drops, and congestion events in real time?
- Which tools do you recommend for root-cause analysis when training slows down?
- Reference architecturesDo you have production-like reference designs for clusters at our size? Are there case studies for workloads that look like ours (LLMs, vision, recommendation, etc.)?
- Do you have production-like reference designs for clusters at our size?
- Are there case studies for workloads that look like ours (LLMs, vision, recommendation, etc.)?
- RoadmapWhat is your timeline for 800G and 1.6T, and how disruptive will upgrades be? How long will this generation of switches/NICs be fully supported?
- What is your timeline for 800G and 1.6T, and how disruptive will upgrades be?
- How long will this generation of switches/NICs be fully supported?
Quick FAQ: Ethernet vs. InfiniBand vs. Omni-Path
Is Ethernet fast enough for AI training?
Yes—when it is designed correctly. With 400G/800G links, RoCEv2, and well-tuned congestion control, Ethernet can support high-performance AI fabrics for many cluster sizes, especially up to the low thousands of GPUs.
Why do some AI superclusters still pick InfiniBand?
InfiniBand offers ultra-low latency, mature collective libraries, and deep integration with high-end GPU systems. For extremely large, tightly-coupled training jobs, that extra performance can translate directly into higher GPU utilization.
What is the real value of Omni-Path in 2025?
Omni-Path targets AI/HPC collectives with strong performance and aims to provide an alternative to both RoCE Ethernet and InfiniBand lock-in. It is most attractive to teams willing to be early adopters in exchange for potential performance and cost advantages.
How can I future-proof my AI data center network? Design around modular spine-leaf architectures; plan for 800G and eventually 1.6T; and keep your backend fabric choices decoupled enough that you can pivot between Ethernet, InfiniBand, and Omni-Path as workloads, economics, and vendors evolve.
Ready to Plan Your AI Fabric?
If you are scoping an AI pod, planning a data center refresh, or just trying to understand what you should be buying next, the Sky High Supply team can help you translate architecture into a concrete bill of materials.
Start with the essentials:
Then build out your AI pod blueprint and scale from there. The network war behind AI is already underway—your fabric choices today will decide whether your GPUs sprint or crawl.