As synthetic intelligence infrastructure scales at breakneck pace, outdated assumptions about networking proceed to flow into. Many of those myths stem from applied sciences designed for a lot smaller clusters, however the recreation has modified. Right this moment’s AI methods are pushing into lots of of 1000’s and shortly, tens of millions of GPUs. Previous fashions merely don’t maintain up.
Let’s take a more in-depth have a look at essentially the most persistent misconceptions about AI networking and why Ethernet has clearly established itself as the inspiration for contemporary large-scale coaching and inference.
Fable #1: Ethernet Can’t Ship Excessive-Efficiency AI Networking
This one’s already been disproven. Ethernet is now the usual for AI at scale. Practically all the world’s largest GPU clusters constructed up to now 12 months use Ethernet for scale-out networking.
Why? As a result of Ethernet now rivals and sometimes outperforms alternate options like InfiniBand, whereas providing a stronger ecosystem, vendor range, and sooner innovation. InfiniBand wasn’t designed for the intense scale we see at present; Ethernet is flourishing with 51.2T switches in manufacturing and Broadcom’s new 102.4T Tomahawk 6 setting the tempo. Large clusters of 100K GPUs and past are already working on Ethernet.
Fable #2: You Want Separate Networks for Scale-Up and Scale-Out
That was true when GPU nodes had been tiny. Legacy scale-up designs labored once you had been connecting two or 4 GPUs. However at present’s architectures typically embody 64, 128, or extra GPUs inside a single area.
Utilizing separate networks provides complexity and value. Ethernet permits you to unify scale-up and scale-out on the identical cloth, simplifying operations and enabling interface fungibility. To speed up this convergence, we launched the Scale-Up Ethernet (SUE) framework to the Open Compute Challenge, shifting the trade towards a single AI networking customary.
Fable #3: Proprietary Interconnects and Unique Optics Are Important
Not anymore. Proprietary approaches might have match older, mounted methods, however trendy AI requires flexibility and openness.
Ethernet supplies a broad set of selections: third-gen co-packaged optics (CPO), module-based retimed optics, linear drive optics, and long-reach passive copper. This flexibility helps you to optimize for efficiency, energy, and economics with out being locked right into a single path.
Fable #4: Proprietary NIC Options Are Required for AI Workloads
Some AI clusters lean on programmable, high-power NICs for options like congestion management. However typically, that’s compensating for a weaker switching cloth.
Fashionable Ethernet switches, together with Tomahawk 5 and 6, already embed superior load balancing, telemetry, and resiliency — lowering price and energy draw whereas leaving extra sources accessible for GPUs and XPUs. Trying forward, NIC features will more and more combine into XPUs themselves, reinforcing the technique of simplifying moderately than over-engineering.
Fable #5: Your Community Should Match Your GPU Vendor
There’s no purpose to tie your community to your GPU provider. The biggest hyperscaler deployments worldwide are constructed on Ethernet.
Ethernet allows flatter, extra environment friendly topologies, helps workload-specific tuning, and is totally vendor-neutral. With its standards-based ecosystem, AI clusters can scale independently of GPU/XPU choice-ensuring openness, effectivity, and long-term scalability.
The Takeaway:
Networking is not a facet word; it’s a core driver of AI efficiency, effectivity, and progress. In case your assumptions are rooted in five-year-old architectures, it’s time to replace your playbook.
The fact is obvious: the way forward for AI networking is Ethernet and that future is already right here.
(This text has been tailored and modified from content material on Broadcom.)
