“Ethernet’s New Dawn: ESUN Aims to Reinvent Networking for Scale-Up AI”
When was the last time Ethernet made headlines in AI hardware? Today. At OCP 2025, the Open Compute Project unveiled ESUN — Ethernet for Scale-Up Networking — a bold, open-standards initiative meant to deliver the ultra-low-latency, high-throughput interconnects that next-gen AI systems demand.
Why ESUN Matters: Closing the Gap in AI Interconnects
Modern AI workloads increasingly demand “scale-up” communication — the type of extremely tight coupling among dozens, hundreds, or thousands of accelerators (GPUs, NPUs, XPUs) within a cluster or rack. In contrast to “scale-out” (spread across servers), scale-up requires brutally low latency, lossless transport, and streamlined protocols suited for collective operations.
Historically, proprietary fabrics (e.g. InfiniBand derivatives, custom interconnects) have dominated this space. But as AI proliferates beyond hyperscale operators, cost, interoperability, and vendor lock-in become bigger barriers.
That’s where ESUN enters the picture: a new OCP workstream centered on adapting Ethernet — a mature, broadly supported networking standard — for the extreme demands of scale-up AI. The goal? Marry the openness and ecosystem leverage of Ethernet with the performance mindset of AI fabrics. (Open Compute Project)
Inside ESUN: What It Will (and Won’t) Tackle
What ESUN Focuses On
ESUN zeroes in on network-level issues — not application logic, not host stacks:
- L2 / L3 framing and switching: defining how Ethernet packets are formed, routed, and switched across hops with minimal overhead. (Open Compute Project)
- Error handling & lossless transport: ensuring packets aren’t dropped, especially in topologies where even microbursts or small packet loss can crush performance. (Network World)
- Interoperability: aligning switch ASICs and XPU (accelerator) network interfaces across vendors. (Open Compute Project)
- Standards alignment: collaborating with IEEE 802.3, UEC (Ultra Ethernet Consortium), and other bodies to maintain open consistency. (Open Compute Project)
What ESUN Does Not Do
To keep scope manageable and avoid overlap, ESUN deliberately excludes:
- Host-side stacks (driver or operating system layers)
- Proprietary or non-Ethernet protocols
- Application- or compute-layer logic
- Non-open architectures or closed vendor solutions (Open Compute Project)
Complementarily, OCP’s SUE-Transport (SUE-T) workstream handles endpoint behavior (like load balancing, transaction packing) and will interface with ESUN when applicable. (Open Compute Project)
Key Players & Ecosystem Join-In
ESUN already boasts heavyweight founding members: AMD, Arista, ARM, Broadcom, Cisco, HPE Networking, Marvell, Meta, Microsoft, NVIDIA, OpenAI, and Oracle. (Open Compute Project)
Notably, Cisco reaffirmed its commitment, pointing to ESUN as a vehicle to advance open Ethernet scale-up without closed silos. (Cisco Blogs)
On Meta’s side, the ESUN launch aligns with their evolving AI networking stack: Disaggregated Scheduled Fabric (DSF), Non-Scheduled Fabric (NSF), and the introduction of 51T switches (e.g. Minipack3N) all point to an AI-first data center vision. (Engineering at Meta)
Through this collaborative model, ESUN aims to accelerate adoption, push experimentation, and create shared tools and reference designs across industry players. (Open Compute Project)
Challenges & What’s Next
While promising, ESUN faces nontrivial hurdles:
- Latency budget is unforgiving: In a multi-hop setup, every nanosecond counts; protocols must be razor-sharp.
- Congestion & flow control: AI workloads can warp traffic patterns; existing flow control (e.g. PFC, LLR, credit-based) may need refinement. (Arista Networks Blog)
- Vendor coordination: Getting ASIC, switch, and XPU vendors to align must overcome competitive incentives.
- Standards convergence: Ensuring that ESUN’s specs can interoperate with global Ethernet efforts is a delicate balancing act.
In the short term, ESUN will kick off working sessions and public calls via the OCP Networking Project. (Open Compute Project)
For AI infrastructure designers and networking engineers, the call is clear: engage now, steer the spec, and build early testbeds.
Why This Matters to the Broader AI Landscape
- Ecosystem leverage: Ethernet already has decades of support in software, silicon, optics, and operations. Reusing and extending it is cheaper than reinventing from scratch.
- Openness vs. lock-in: A shared, standards-based interconnect reduces the risk of vendor lock-in, making AI more accessible beyond hyperscalers.
- Future-proofing: If ESUN succeeds, it could unify the cluster-scale interconnect for emerging models, reducing fragmentation in AI hardware stacks.
- Bridging scale-up and scale-out: With Ethernet already dominant in data centers, ESUN offers a path to unify intra-node and inter-node networking under one paradigm.
Glossary
| Term | Definition |
|---|---|
| Scale-up | Networking among closely coupled accelerators (within a rack or cluster). |
| XPU | Generic term for accelerator units like GPU, NPU, TPU, etc. |
| L2 / L3 framing | Layers 2 and 3 in the OSI model — dealing with Ethernet frames and IP packet routing. |
| PFC (Priority-based Flow Control) | Mechanism to prevent packet drops by pausing per-priority traffic. |
| LLR (Link-Layer Retry) | A local retry mechanism to recover from errors at the link layer. |
| SUE-T (Scale-Up Ethernet Transport) | OCP workstream for endpoint-side enhancements (e.g. load balancing, buffer management). |
| UEC (Ultra Ethernet Consortium) | Industry group focused on advancing Ethernet for exotic use cases. |
Ethernet’s next frontier may be the very medium that powered the Internet for decades. Through ESUN, the industry is re-engineering it to serve as the nervous system for tomorrow’s AI supercomputers — combining openness, performance, and scale.
