NVIDIA Networking

Agentic AI is changing the rules for inference. With DeepSeek V4, NVIDIA Blackwell delivered 20x lower cost per token out of the box, running a 1.6T parameter MoE model with a 1M token context on day one.

But the real story is how:

NVIDIA is the only platform co-designed end-to-end across five rack-scale systems—engineered to operate as a unified AI factory rather than a collection of discrete components.

That’s what enables:
→ Higher throughput for agentic workloads
→ Lower latency across multi-step reasoning loops
→ Sustained improvements in token economics over time

As AI factories scale, cost per token becomes the metric that matters and extreme co-design is the advantage that compounds.

📗 https://nvda.ws/4f1JRHl

Q: What's the backbone of every AI factory? 🏭

A: The network!

NVIDIA Vera Rubin brings NVLink 6, ConnectX-9, BlueField-4, and Spectrum-6 together into one tightly integrated fabric.

✅ 3.6 TB/s of GPU-to-GPU bandwidth
✅ Proactive congestion control
✅ A software-defined infrastructure layer built to eliminate idle time at scale

Step inside the NVIDIA Vera Rubin platform 👉 https://nvda.ws/4tdQzNT

Copper and fiber aren't competing. They're dividing the job.

Gilad Shainer, SVP of Networking at NVIDIA, breaks down how copper and fiber each play a distinct role in AI data center design, and why co-packaged optics is changing the power equation.

Read the Network World article to learn more ➡️ https://nvda.ws/3ON8rRC