Today’s most important event is NVIDIA GTC Conference, which is basically an AI version of A Short History of Humanity.

The most important thing today is NVIDIA’s GTC conference—basically an AI version of A Brief History of Humankind.

Jensen Huang hasn’t even taken the stage yet, but the information leaked early is already enough to fill a whole book.

Wanwán has put together three big highlights—come on, friends, follow me.

1. AI computing power costs directly cut to one-tenth

The previous generation Blackwell was already very impressive, right? Next up, the new-generation chip Vera Rubin is about to begin mass production.

So what makes Vera Rubin so strong? In plain terms, just two words: cheap.

Running the same AI model,
the number of chips is reduced to one quarter, and inference computation costs drop by 90%.
Drop by 90%, friends.
AWS, Microsoft, and Google—the three major cloud providers—move onto board in the first batch.

2. The Groq deal worth 20 billion USD last year—handing in the assignment today

Previously, at the earnings call, Jensen Huang said Groq would be integrated into the NVIDIA ecosystem as an expansion architecture—just like how back then they acquired Mellanox to round out networking capabilities.

Groq’s LPU and NVIDIA’s GPU sit in the same data center: the GPU understands the problem, while the LPU rapidly spits out the answers.

With the two chips split up and working together, the latency for Agent scenarios drops directly.

AI Agents do the work for people. A single task might involve adjusting the model dozens of times, and each round is burning inference computing power—while users are right there waiting. If it’s even a bit slower, the experience collapses.

Inference comes in two steps: first, understand your question; then output the answer word by word.

GPUs are good at the first step, but for the second step—how fast and stable it is at producing words—the Groq LPU is stronger.

Is 20 billion USD expensive?

Think about it: in the future, every company will run hundreds of Agents, and each Agent will adjust models thousands of times every day.

3. NVIDIA’s OpenClaw version goes live—called NemoClaw

It’s basically an open-source platform. Enterprises can install it to deploy AI employees that run workflows, handle data, and manage projects. It’s said they’re already in talks with Salesforce and Adobe.

The interesting part is that NemoClaw doesn’t require you to use NVIDIA chips. You should think about that logic. Selling chips only earns you money on the hardware layer; setting the rules is what lets you earn across the entire chain. Jensen Huang has done the math very clearly.

4. Jensen Huang says he will demonstrate “a chip the world has never seen before”

Most likely, the next-next generation architecture, Feynman, will make its first appearance, with mass production in 2028 using TSMC’s most advanced 1.6nm process.

Also, there’s one more under-the-radar piece of information I think is pretty interesting.

NVIDIA has released laptop computer processors—two models—focused on gaming. The company selling graphics cards is coming to grab the CPU market’s share.

Wanwán, I feel like Jensen Huang is going to become a great figure of an era in the future.