The Interconnect Revolution – The $7.3B Surprise

If Layer 1 of the AI stack redefined the atomic unit of compute as the datacenter, then Layer 2 explains why that unit can even function: interconnects. GPUs are powerful in isolation, but AI workloads—training trillion-parameter models and running distributed inference at scale—demand that thousands of GPUs operate not as independent islands, but as a single synchronized brain. The bottleneck has shifted. Where Moore’s Law once focused attention on transistor density, and Layer 1 on compute throughput, Layer 2 reveals the new constraint: data movement.

NVIDIA’s Q2 FY2026 results make this shift undeniable. Networking revenue surged to $7.3B—a 198% year-over-year increase and 146% quarter-over-quarter jump. What was once an auxiliary component of datacenter design has become a critical driver of value creation.

From Compute Bottleneck to Communication Bottleneck

In the early AI wave, the bottleneck was compute. The industry solved this with massively parallel GPUs. But as clusters grew to tens of thousands of nodes, the bottleneck migrated: latency and bandwidth between GPUs became the limiter.

• Previous bottleneck: Limited compute throughput per chip.
• New bottleneck: Moving data across GPUs fast enough to maintain synchronization.

This matters because AI workloads are inherently collective tasks. Training a large model requires not just raw FLOPs but continuous sharing of gradients, parameters, and activations across GPUs. Any slowdown in data transfer cascades into wasted compute capacity.

In other words, the value of a GPU is increasingly defined not by its internal power, but by how well it connects to its peers.

Enabling Technologies

NVIDIA’s interconnect strategy has three pillars, each now central to the AI economy:

1. NVLink Fabric (GB200/300): Proprietary, high-speed GPU-to-GPU communication fabric that turns clusters into coherent supercomputers. NVLink eliminates PCIe bottlenecks and allows GPUs to communicate as if they share memory.
2. XDR InfiniBand: High-throughput, low-latency networking standard, ramping fast to support distributed training at scale. InfiniBand is particularly critical for hyperscalers and research labs training frontier models.
3. Ethernet for AI (CSP adoption): Traditional data networks adapted for AI workloads. While not as performant as InfiniBand, Ethernet provides scalability, cost efficiency, and wide adoption, making it critical for cloud service providers.

Together, these interconnect layers are what make the datacenter the atomic unit of compute. Without them, GPUs are just expensive silos. With them, they become parts of a cohesive computational fabric.

The $7.3B Surprise

Why is networking revenue exploding? Three structural reasons:

1. Training Scale: Models like GPT-5, Claude, or Gemini require hundreds of thousands of GPUs. Without advanced interconnect, synchronization would collapse. Networking has become a prerequisite for frontier AI.
2. Inference Distribution: It’s not just training—distributed inference is exploding. Running AI workloads for millions of users in real time demands parallelization at scale, where communication overhead dominates costs.
3. System Design Shift: Hyperscalers now design datacenters as tightly coupled supercomputers rather than loose clusters. That makes interconnect a core budget line item, not an afterthought.

The result: $7.3B in quarterly networking revenue, growing faster than compute itself. In effect, networking is now as critical to AI economics as GPUs.

Bottleneck Economics

Interconnect is no longer a supporting role—it is the critical path.

• If compute improves 20% but interconnect lags, overall system performance may stall or even regress.
• If interconnect scales faster, the effective value of existing GPUs compounds.

This is why NVIDIA’s control over NVLink and InfiniBand matters strategically. By tying compute and interconnect into one package, NVIDIA ensures customers can’t just swap in competitors’ GPUs without breaking the system. It’s vertical integration at the datacenter layer.

Strategic Implications

Layer 2 introduces new forms of competitive dynamics and systemic risk:

1. NVIDIA’s Moat Extends Beyond CUDA: CUDA gave NVIDIA dominance at the software layer, but NVLink and InfiniBand entrench it further at the physical fabric layer. Competitors can’t just match GPU specs; they must replicate the entire networked ecosystem.
2. Hyperscaler Dependence: Cloud providers are locked into NVIDIA’s interconnect stack if they want performance parity. This raises switching costs and deepens dependency.
3. Commoditization Risk: Ethernet adoption signals a parallel path where hyperscalers may eventually push for open standards, reducing reliance on proprietary fabrics. This could create an open vs closed interconnect battle, echoing the software platform wars.
4. Geopolitical Leverage: Export controls on advanced GPUs now implicitly extend to networking gear. A country that gains GPUs without equivalent interconnect capacity still cannot compete at frontier scale. Interconnect becomes a chokepoint of national AI capacity.

The Bigger Picture

The Interconnect Revolution reframes how we think about AI progress. For decades, the focus was on faster chips. Today, the critical question is: how well do those chips talk to each other?

• Datacenter efficiency is now measured less by raw GPU count and more by communication efficiency per dollar.
• Innovation velocity is tied to bandwidth and latency gains, not just FLOPs.
• Economic leverage shifts from chip units sold to fabric architectures deployed.

This makes Layer 2 both a technical necessity and a strategic fulcrum.

Conclusion

Layer 2, the Interconnect Revolution, represents the invisible backbone of the AI economy. With $7.3B in networking revenue growing faster than compute, NVIDIA has proven that interconnects are no longer peripheral—they are the lifeblood of AI-scale computing.

The bottleneck has shifted from compute to communication. That shift creates both opportunity and risk: companies that master interconnect design control the scalability of AI itself, while those that ignore it risk being left with stranded compute assets.

The lesson is simple but profound: AI’s future will be determined not just by how powerful individual GPUs become, but by how seamlessly they work together.