The AI Infrastructure Dilemma and the Battle for AI Networking

What is AI Infrastructure? Why Does It Matter?

If you’re running a modern enterprise, chances are AI has found its way onto your strategic roadmap—whether it’s automating customer support, crunching real-time analytics, or training massive machine-learning models. But AI isn’t like your traditional IT workloads.

Running AI at scale requires an entirely different class of infrastructure, one more akin to high-performance computing (HPC) than to traditional cloud computing. We’re talking about:

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• Massive compute clusters packed with GPUs (or even TPUs, if you’re in the Google camp).
• High-speed storage to feed those hungry models with terabytes of data.
• Ultra-fast networking fabric to connect everything seamlessly.

This last part—networking—is where things get interesting. There’s an ongoing battle for the backbone of AI infrastructure, and it’s shaping up to look a lot like the iPhone vs. Android war.

InfiniBand: The Apple iPhone Approach to AI Networking

InfiniBand is the premium, high-performance option—a tightly integrated, vendor-controlled ecosystem where everything “just works” because one company (mostly) calls the shots.

• Think of InfiniBand like the iPhone: a vertically integrated stack where hardware, software, and networking are designed to work together perfectly.
• Apple controls the whole experience, ensuring high performance but at the cost of flexibility and premium pricing.
• You’re locked into Apple’s ecosystem. Want to switch to Android? You’ll have to start over with a new device, a new OS, and a different app store.

Now apply that logic to InfiniBand:

• InfiniBand is the “premium” AI networking solution, originally designed for HPC, now widely used for AI workloads.
• It’s high-performance, low and stable latency, and optimized for large-scale compute clusters.
• Nvidia owns it now (after acquiring Mellanox), meaning it’s deeply embedded in Nvidia’s AI stack.
• But it’s a (mostly) closed ecosystem. To use InfiniBand, you need InfiniBand-specific hardware, and you’re tied to Nvidia’s roadmap and pricing.

For companies building high-end, performance-first AI clusters, InfiniBand is often the default choice—just like for consumers who want a seamless iPhone experience. But there’s another option. This is also largely due to Nvidia’s Reference Architecture specifying InfiniBand as the network fabric for interconnecting compute nodes.

RoCE: The Android Approach to AI Networking

RoCE (RDMA-over-Converged-Ethernet) is the Android of AI networking—built on open, flexible, and cost-efficient principles.

• Android is open-source and has a multitude of vendors.
• There’s a huge range of devices—from budget phones to Samsung’s ultra-premium Galaxy lineup.
• But not all Android experiences are created equal—some implementations are fantastic, while others are, well… not.

RoCE follows the same philosophy:

• It runs on Ethernet fabric—the standard networking fabric that powers most enterprise data centers.
• It’s open and flexible, meaning companies can integrate it into existing network architectures without being locked into a single vendor’s ecosystem.
• Performance (for AI) can vary, though. While top-tier implementations rival InfiniBand, lower-end deployments struggle with congestion control, priority flow control, and occasionally latency.

That said, RoCE is gaining serious momentum. Hyperscalers like Meta, Tesla, and others are actively investing in RoCE-based AI networking, with the Ultra Ethernet Consortium (UEC) working to expand RoCE (standards wise) to close the gap between RoCE and InfiniBand for AI.

So, What’s the Right Choice for Your AI Strategy?

• If you have existing infrastructure with InfiniBand fabric already in place – InfiniBand is an obvious choice.
• If you value flexibility, scalability, and cost-efficiency, RoCE is worth considering—especially as UEC continues optimizing it for AI.
• Hyperscalers (and others) are making a clear bet on RoCE, signaling that open networking could be the future of AI infrastructure.

The battle isn’t settled yet, but one thing’s clear: AI networking is at a crossroads, and enterprises need to decide which path they want to take.

Join Us at Nvidia GTC

If you’re looking to avoid the fate of companies that clung to siloed architectures and missed the hyperscale boat, don’t repeat the past. Instead, discover how to build on the lessons learned by hyperscalers and apply them to the AI revolution.

NVIDIA #GTC2025 Conference Session Catalog

Attend our session, “Beyond Silos: Unlocking AI’s Full Potential with Petabit-Scale Data Mobility,” Tuesday, Mar 18 4:20 PM – 4:35 PM PDT and learn how interconnected, elastic infrastructures are transforming AI at every level. We’ll dissect:

• Why traditional cloud computing creates bottlenecks for AI
• How a petabit-scale platform accelerates data mobility
• The blueprint for building an interconnected compute model