Shipping today

Is Ethernet ready for AI? The proof is shipping

The four sections below each answer it with something already in production, not a roadmap: shipping 800G silicon, a hyperscale Ethernet cluster, the closed utilization gap, and a moving standard. Every figure is sourced to a hardware spec or a named third party.

51.2 TbpsTomahawk 5 per switch, 64 x 800G
24,576 GPUsMeta's published Ethernet AI cluster
90%+Fabric utilization target with DLB
UEC 1.0Open Ethernet transport spec, June 2025
Is open Ethernet ready for production AI fabrics? Yes, and the evidence is shipping. The 800G silicon is in HCL-listed open switches now, hyperscalers already run AI training on Ethernet at 24,576-GPU scale, the load-balancing utilization gap is closed, and the Ultra Ethernet standard is moving. OcNOS-DC runs the lossless RoCEv2 fabric today, with software and qualified hardware under one IP Infusion contract when bought as OcNOS Systems.
Proof 1 · The silicon

The hardware is here

Open 800G AI switches are not a preview. They are on the hardware compatibility list and shipping. OcNOS-DC runs on Broadcom Tomahawk 5 and Tomahawk 4 platforms from Edgecore and UfiSpace, the same merchant silicon the rest of the industry builds AI fabrics on, so you keep switch, NIC, and GPU choices open. These are on-chip, shared-buffer switches built for lossless RDMA at 800G.

BCM78900 · 51.2 Tbps · 64 x 800G

800G spine and leaf

Edgecore AIS800-64D and UfiSpace S9321-64E / S9321-64EO, the latter with 400G ZR+ coherent ports. On-chip shared buffering for lossless RoCEv2. Both platforms are HCL-listed.

Broadcom Tomahawk 5
BCM56990 · 25.6 Tbps · 64 x 400G

400G leaf

Edgecore AS9736-64D for 400G leaf roles and smaller fabrics, on the same lossless RoCEv2 primitives as the 800G tier, breaking out to 100G and 25G.

Broadcom Tomahawk 4
Software + qualified hardware

Supported as a system

With OcNOS Systems, one IP Infusion contract covers OcNOS-DC and the qualified switch hardware together, so there is one TAC and one SLA across the whole fabric.

Proof 2 · The scale

The scale is proven

The most common objection to Ethernet for AI is scale, and the industry has already answered it in production. You do not have to take a switch vendor's word for it, because the hyperscalers publish their designs.

Source: Meta's published AI cluster

Meta runs a 24,576-GPU AI training cluster on an Ethernet-based fabric. That is industry proof that standard Ethernet carries hyperscale AI training at scale, in production, today. It is the reference point that settles the readiness question: the fabric is not the limit.

On OcNOS-DC the same physics apply to your build. A two-tier Tomahawk 5 design reaches 4,096 GPUs at 800G per GPU and a 1:1 subscription ratio. Above that, a super-spine tier extends to 16,000 or more. See the AI fabric topologies for the port math behind each tier.

Proof 3 · The utilization gap

The utilization gap is closed

The old knock on Ethernet was load balancing: standard ECMP pins each large flow to one uplink, so during a collective some links jam while parallel paths sit idle and the sync waits on the slowest link. That gap is now closed on the same switches, and the proof is a hardware feature, not a promise.

90%+ target

How the gap closes. Static ECMP pins each large flow to one uplink; dynamic load balancing rebinds flows on real-time congestion so a well-run AI fabric targets utilization in the 90 percent range on Tomahawk 4 and Tomahawk 5, on the same switches, with no extra uplinks. OcNOS-DC ships DLB on those platforms.

DLB deep-dive →
Proof 4 · The standard

The standard is moving

Ethernet for AI is not a single vendor's proprietary detour. The Ultra Ethernet Consortium published the UEC 1.0 specification to adapt Ethernet transport for AI and HPC scale, backed by the companies that build the silicon, the NICs, and the clouds. IP Infusion is a member of the Ultra Ethernet Consortium.

UEC 1.0 defines a multipath transport that spreads one flow across many paths instead of pinning it to a single ECMP hash, with that endpoint behavior running on UEC-capable NICs. OcNOS-DC runs lossless RoCEv2 with PFC, ECN marking, and DLB in production today on Broadcom Tomahawk 4 and Tomahawk 5 switches. DCQCN runs on the NICs and reacts to those ECN marks. See the Ultra Ethernet deep-dive for how packet spray and multi-path RDMA change the transport.

Proof 5 · Ready to deploy

What OcNOS-DC ships today

Underneath the proof points is a fabric you can turn up now. OcNOS-DC provides the lossless Ethernet fabric that RoCEv2 requires, so your training collectives move GPU-to-GPU over RDMA without loss. DCQCN is the end-to-end loop between the NICs and the switches; the switch role in that loop is early ECN marking. Every primitive below is verified and shipping, not a roadmap item.

PFC

Priority flow control, including over L3, with DCBX and LLDP.

ECN and Dynamic ECN

Early congestion marking, the switch side of the DCQCN loop.

ETS

Enhanced transmission selection to keep RDMA flows ahead.

WRED

Weighted random early detection for queue management.

DLB

Dynamic load balancing with Reactive Path Rebalance and Random Flow.

PFC Deadlock Detection and Recovery

Detects paused-queue cycles and auto-drains before jobs hang.

Buffer tuning

Switch packet buffer tuning on the on-chip shared buffer.

RTAG7 hashing

Fine-grained hashing for even spread across the fabric.

gNMI / OpenConfig telemetry

Streaming telemetry over OpenConfig YANG for closed-loop tuning.

Verified, shipping in OcNOS-DC
On the roadmap

What is coming next

The items below are roadmap and upcoming-silicon, labeled as such. They are not shipping capabilities today, and none of the figures are IP Infusion performance numbers.

Global Load Balancing (GLB)

Extends DLB fabric-wide: spines publish path-quality telemetry back to ingress leaves so routing scores the full multi-hop path.

Roadmap: OcNOS 7.1

Latency-based ECN

Congestion marking driven by measured latency, adding to the existing ECN and Dynamic ECN behavior.

Roadmap: OcNOS 7.1.0

LLR, CBFS, packet trimming

Link-level retry, credit-based flow control, and packet trimming, for loss recovery and congestion handling at the link and switch.

Roadmap

Broadcom Tomahawk 6

BCM78910 / BCM78914, 102.4 Tbps, 3nm. Announced by Broadcom. OcNOS support planned.

Roadmap

Follow the evidence further

Each proof point above has a deeper technical page. Compare the transports head to head, size a fabric for your GPU count, or read how the lossless behavior is built.

AI fabric proof FAQ

Is Ethernet ready for production AI fabrics?
Yes. The proof is already deployed rather than promised. Open 800G switches built on Broadcom Tomahawk 5 (51.2 Tbps, 64 x 800G) are listed on the hardware compatibility list and shipping today. Hyperscalers run large AI training clusters on Ethernet in production, including Meta's published 24,576-GPU Ethernet-based cluster. OcNOS-DC provides the lossless RoCEv2 fabric those workloads need, with PFC, ECN, and dynamic load balancing that you can deploy now.
Can Ethernet match InfiniBand for AI workloads?
For the fabric behavior AI training depends on, Ethernet carries the same lossless RDMA transport and closes the load-balancing gap that used to separate the two. Meta's published 24,576-GPU cluster is industry proof that Ethernet carries hyperscale AI training in production. On the utilization side, dynamic load balancing rebinds flows on real-time congestion so a well-run fabric holds utilization in the 90 percent range on the same switches, where static hashing would strand capacity. The difference now is that Ethernet stays open and multi-vendor while InfiniBand is single-vendor.
What scale is proven on Ethernet for AI training?
Meta's published Ethernet-based AI training cluster runs 24,576 GPUs, which is direct industry proof that standard Ethernet carries hyperscale AI training. On OcNOS-DC, a two-tier Tomahawk 5 design reaches 4,096 GPUs at 800G per GPU and a 1:1 subscription ratio. Above that, a super-spine tier extends to 16,000 or more.
What does OcNOS-DC support today for AI fabrics?
OcNOS-DC ships the lossless RoCEv2 primitives a 2026 AI workload needs, ready to deploy: PFC (including over L3, with DCBX and LLDP), ECN and Dynamic ECN, ETS, WRED, dynamic load balancing (DLB) with Reactive Path Rebalance and Random Flow, PFC Deadlock Detection and Recovery, buffer tuning, RTAG7 hashing, and gNMI and OpenConfig telemetry. These are verified, shipping capabilities, not roadmap items.
Where does OcNOS-DC stand on Ultra Ethernet (UEC)?
IP Infusion is a member of the Ultra Ethernet Consortium (UEC). OcNOS-DC runs lossless RoCEv2 with PFC, ECN marking, and DLB in production today on Broadcom Tomahawk 4 and Tomahawk 5 switches. DCQCN runs on the NICs and reacts to those ECN marks. The Ultra Ethernet transport runs on UEC-capable NICs. To plan how UEC fits a specific build, talk to an IP Infusion engineer.
Can I deploy an open Ethernet AI fabric now?
Yes. The switches are HCL-listed and shipping, the lossless RoCEv2 fabric runs in OcNOS-DC today, and with OcNOS Systems one IP Infusion contract covers the software plus the qualified hardware, so there is one TAC and one SLA. You keep your GPU, NIC, and switch choices open.

Go deeper. Take it with you.

Two short, technical downloads that go further than this page: the full OcNOS-DC datasheet and the lossless 800G AI fabric architecture.

Datasheet

OcNOS-DC Datasheet

Full OcNOS-DC specification: the EVPN-VXLAN and Ethernet for AI feature set, software SKUs, supported hardware platforms, and the solution ordering guide.

Get the datasheet
Solution Brief

OcNOS 800G Lossless AI Fabric

Lossless RoCEv2 fabric on Tomahawk 5 spines: SKU tiers, qualified platforms, and deployment architecture.

Get the brief

Ready to put the proof to work? Let's design your fabric.

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