Nvidia’s Vera Rubin platform — the successor to Blackwell — is now in full production. The headline number is 3.6 exaflops of AI compute, a figure so large it demands context.

Blackwell redefined what enterprise AI clusters could look like. Vera Rubin doubles down: a Grace-class CPU paired with next-generation Rubin GPUs delivers not just raw throughput but a dramatic efficiency gain. When paired with Groq’s third-generation LPU, the combination yields 35× the throughput per megawatt versus prior configurations.

The DGX Station — a desktop-class AI workstation based on Grace Blackwell — opened pre-orders on February 19 at approximately $100,000. That price point signals something important: Nvidia isn’t just selling data-centre racks. They’re selling personal supercomputers to the enterprise developer.

The real announcement at GTC 2026 may not be a chip at all. OpenClaw is Nvidia’s open-source agentic AI framework, already being described internally as the “fastest-growing open-source project in history.”

Here is what makes OpenClaw significant: it mirrors the primitives of a traditional operating system, but for AI agents.

This is not incremental. This is Nvidia attempting to own the abstraction layer above hardware — the same move Microsoft made with Windows, Apple made with iOS, and Google made with Android.

If OpenClaw is the open-source OS kernel, NemoClaw is the enterprise distribution. It sits on top of OpenClaw and adds:

Think Red Hat Enterprise Linux to OpenClaw’s Fedora. The open-source community builds the innovation; NemoClaw is how CIOs justify the purchase order.

Nvidia has raised its internal AI compute demand forecast from $500 billion to $1 trillion. That is not a rounding error — it is a structural revision reflecting how quickly enterprise and hyperscaler demand has outpaced every prior model.

The catalyst is the agent wave. Inference workloads for single-turn chat are predictable and relatively cheap. Agentic workloads — where a single user request spawns dozens of sub-agent calls, tool invocations, and retrieval passes — are multiplicatively more expensive. Every enterprise deploying agentic AI needs more compute than they planned for.

The Nemotron 4 model isn’t being built by Nvidia alone. A coalition of AI-native companies is co-developing it:

This coalition structure is deliberate. By embedding Nemotron 4 into the workflows of the most widely-used AI developer tools — Cursor for coding, LangChain for agent orchestration, Perplexity for search — Nvidia ensures model adoption follows platform adoption.

GTC 2026 has 450 sponsors, 2,000 speakers, and 1,000 technical sessions. The conference has quietly become the most important gathering in enterprise technology. Jensen Huang’s keynote — likely to run over two hours — is expected to be the defining moment of the AI agent era.