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InfiniBand or Ethernet for Private AI: What Australian Buyers Actually Need to Know in 2025

A source-backed analysis of the InfiniBand versus Ethernet debate for private AI infrastructure, framed for Australian enterprise and data centre buyers evaluating GPU cluster fabrics, RoCE v2 deployment, and open

By xSONiC Team · · SONiCopen networkingAI fabricEthernet

What Happened: Ethernet Is Closing the Gap on InfiniBand for AI Workloads

Why It Matters: Private AI Buyers Face a Fabric Decision Early in the Build

When an Australian enterprise commits to a private AI deployment — whether that is a GPU inference server rack for RAG and LLM workloads, or a multi-node training cluster for domain-specific models — the network fabric is one of the first architectural decisions. It determines which switch hardware you can buy, which NOS you can run, what optical transceivers and cabling you need, and how your operational team manages the environment day-to-day. InfiniBand delivers deterministic low-latency, adaptive routing, and GPUDirect RDMA out of the box. It is proven at scale. But it also locks buyers into a narrower hardware ecosystem, requires specialised skills, and typically commands a price premium at every layer of the stack: switches, host channel adapters, cables, and optics. Ethernet, by contrast, is the protocol every data centre team already understands. With RoCE v2, Data Centre Bridging (DCBX), Explicit Congestion Notification (ECN), Priority Flow Control (PFC), and congestion management features like Fast Congestion Notification Protocol (CNP), Ethernet fabrics can now support lossless, low-latency RDMA traffic suitable for distributed AI training and inference. The key question for buyers is no longer ‘can Ethernet do AI?’ but rather ‘at what scale and with what trade-offs does Ethernet make more sense than InfiniBand for my workload?‘

The xSONIC Buyer Angle: Open Ethernet with SONiC Gives You Optionality

For organisations evaluating private AI infrastructure in Australia, the InfiniBand-versus-Ethernet decision has a third dimension that is often overlooked: network operating system openness. SONiC, the open-source NOS backed by the Linux Foundation and the Open Compute Project (OCP) Networking project, runs on switches from multiple vendors and ASICs. The OCP Networking project lists SONiC alongside ONIE and SAI as core sub-projects for disaggregated, fully open networking (source: opencompute.org/projects/networking). SONiC’s container-based architecture decouples network functions into modular Docker containers, which provides better fault isolation, easier upgrades, and simplified troubleshooting compared to monolithic switch software (source: sonicfoundation.dev). What makes this relevant to the AI fabric debate is that SONiC supports both BGP and RDMA — meaning it can serve as the NOS on Ethernet switches handling RoCE v2 traffic in GPU backend fabrics. NVIDIA’s own Spectrum Ethernet switches support Pure SONiC alongside Cumulus Linux, giving buyers the choice of an open-source NOS on commercially available AI-optimised switching hardware (source: nvidia.com/en-us/networking/ethernet-switching). For an Australian enterprise that wants to avoid vendor lock-in on its AI network fabric, an Ethernet + SONiC + RoCE v2 stack offers a credible path: you get multi-vendor switch hardware, an open-source NOS with a growing community, and standard Ethernet operational tooling, while still delivering RDMA performance for GPU-to-GPU communication.

Where InfiniBand Still Wins: Scale, Determinism, and Ecosystem Maturity

It would be misleading to suggest that Ethernet has fully caught up with InfiniBand for every AI workload. At the largest scales — think thousands of GPUs in a single training fabric — InfiniBand’s adaptive routing, GPUDirect RDMA, and tightly coupled congestion control still deliver advantages in tail latency and fabric utilisation that are difficult to replicate on Ethernet. NVIDIA’s Quantum InfiniBand platform, including the Quantum-X800 series, is designed for ‘giant AI clusters’ (source: nvidia.com/en-us/networking/ethernet-switching). For hyperscale AI training — the kind of workload run by the largest cloud providers and AI labs — InfiniBand remains the default choice. The operational ecosystem for InfiniBand, including NVIDIA’s Unified Fabric Manager (UFM) for fabric management, is also mature and purpose-built for HPC and AI environments. Australian organisations building at that scale, or planning to scale to thousands of GPUs, should evaluate InfiniBand seriously. But for the majority of private AI deployments in Australia — inference clusters, fine-tuning environments, RAG pipelines, and domain-specific model training on tens to hundreds of GPUs — the performance gap between InfiniBand and a well-designed 400GbE or 800GbE RoCE v2 fabric may not justify the cost premium and operational complexity.

What to Watch: Spectrum-X, SONiC, and the Australian AI Data Centre Build-Out

Three developments make this debate timely for Australian buyers. First, NVIDIA’s Spectrum-X Ethernet platform is purpose-built for AI networking, with the Spectrum-4 SN5000 series offering up to 800Gb/s per port and the new Spectrum-6 SN6000 series incorporating co-packaged silicon photonics for improved power efficiency and resiliency (source: nvidia.com/en-us/networking/ethernet-switching). These are not general-purpose data centre switches; they are designed for GPU cluster backends. Second, SONiC continues to mature as a production-grade NOS. Its origins in the largest cloud data centres in the world mean that features like BGP, RDMA support, and container-based modularity have been battle-tested at scale (source: sonicfoundation.dev, github.com/sonic-net/SONiC). The OCP community and SONiC Foundation ecosystem are actively developing and validating new capabilities. Third, the Australian data centre market is investing heavily in AI-ready infrastructure. The OCP Podcast featured David Hirst, CEO of Macquarie Data Centres, discussing how AI workloads are shifting data centre design from ‘real estate’ to ‘chip-out thinking,’ with liquid cooling and megawatt-per-rack designs becoming the new normal (source: opencompute.org/ocp-podcast, Episode 18, January 2026). Australian colocation providers are building for AI density, which means the network fabric decision is becoming a first-class infrastructure concern rather than an afterthought.

Buyer Decision Framework: When Ethernet Makes Sense for Your AI Fabric

Based on the current state of technology and the Australian market, here is a practical framework for private AI buyers evaluating their fabric options. Choose Ethernet with RoCE v2 if your GPU cluster is in the tens to low hundreds of nodes, you want to use open-source SONiC as your NOS for operational flexibility, your team has Ethernet expertise and wants to avoid InfiniBand skill gaps, you plan to converge your AI fabric and general data centre network on a single protocol, or you want multi-vendor switch hardware options to optimise cost. Consider InfiniBand if you are building at hyperscale (hundreds to thousands of GPUs), your AI workload demands the lowest possible tail latency with zero packet loss, your budget accommodates the InfiniBand hardware and skills premium, or you are deploying a dedicated AI training cluster where fabric performance is the primary bottleneck. For most Australian private AI deployments in the evaluate or plan stage, Ethernet with RoCE v2 on SONiC-based switches offers a strong balance of performance, cost, and operational simplicity. The technology is proven, the ecosystem is growing, and the open-networking model gives you optionality that proprietary InfiniBand fabrics cannot match.

What This Means for xSONIC Product Families

The InfiniBand-versus-Ethernet decision maps directly to several xSONIC product categories and solution pillars. If you are building an Ethernet-based AI fabric, you need data centre AI switches that support RoCE v2 and SONiC, optical transceivers (SFP28, QSFP28, QSFP-DD, OSFP) for 100G/400G/800G links, and potentially bare-metal switches if you want full NOS flexibility. The xSONIC AI Fabric and GPU Backend Fabric solution guides, along with the RoCE v2 and DCBX technology pages, are designed to help buyers navigate exactly these decisions. For Australian buyers in the evaluate stage, the practical advice is straightforward: start with your workload requirements (training vs inference, cluster size, latency sensitivity), then work backwards to the fabric architecture. If Ethernet meets your performance needs — and for most private AI deployments it will — the open networking path with SONiC gives you the widest range of hardware options, the lowest vendor lock-in risk, and the most transferable operational skills.

Sources Reviewed