The entire 5G stack can run in Grace Hopper as a software codec-like platform.


We’ve moved onto video transfer, the compression-stream-decompression paradigm that’s been in use since the 1960s. Using Grace Hopper, you can now have digitized avatars that can translate your voice, render your image in 3D, apply generative filters.


Google Cloud, Meta, and Microsoft are the first customers in line for the GH200 supercomputer.


Grace Hopper is going to revolutionize drug discovery and medicine. I don’t know about all the other industries, but medicine is going to see a massive benefit from this.

You can use Grace Hopper as pods, which can get you 256 GH superchips, with 144TB sharred, connected memory for 1 exaFLOPS of performance. It’s really hard to quantify that for anybody who isn’t really deep into data center.

“I wonder if it can play Crisis,” Jensen says. 


Grace Hopper is in full production. GH200 is capable of 65 billion parameters, which is absolutely obscene. Which has interlinked shared memory, so you don’t have to break data into pieces, which will help scale out AI.


A chart showing the AI hockey stick development

(Image credit: Future / John Loeffler)

Jensen busted out the S curve and hockey stick that anyone who has looked at AI should be very familiar with.


I agree with Jensen, we’re in a new computing era for sure.


1600 generative AI startups partnering with Nvidia. Yeah…Nvidia’s 5000-series graphics cards will probably be Nvidia’s last. Maybe the 6000-series, but shareholders will force Nvidia to go all in on generative AI and AI datacenters.


Jensen is showing off the power of prompts to generate new content, including a text to video demo with a very realistic woman speaking the words. He’s showed off a generative AI demo of a traditional Taiwanese song, and then gave the AI a prompt to create a sing along song. He just made the audience sing along.


WE can now apply computer science…to so many different fields that wasn’t possible before,” he says.


“Anything that has structure, we can learn that language,” Jensen says. “Once you can learn the language of certain information…we can now guide the AI to produce new information.”


ChatGPT has entered the chat.


Jensen says that Nvidia improved graphics processing 1000x in 5 years using AI. I am really not sure where that number is coming from.


Nvidia has been investing heavily in its AI infrastructure, and it’s making major advances every two years on AI supercomputers. These supercomputers will need dedicated programmers, comparable to computer “factories”. To be honest, this keynote is going over the head of probably 60% of the audience at the very least.


LOL, the H100 costs $200,000. Talk about GPU price inflation.


The Nvidia HGX H100 is in full production, Jensen says. To be clear, this is a data center GPU, so don’t expect it to run any of the best PC games on your rig unless you’re playing via the cloud.


Every single data center is overextended, Jensen says. I don’t doubt that one bit.


Nvidia is going to transition into a data center AI company as a primary function of its business, and it looks increasingly like graphics cards are going to be more of an afterthought.

It makes sense. Given how much money Nvidia is making off its AI side of the business, the market incentives to go all-in on AI will be irresistable.


Jensen is showing off a datacenter GPU server that can run a large language model for less then 0.4 GWHr for $400K.


After the brief talk about ray tracing and whatnot, we’re getting to the meat of Nvidia’s new business model: data center GPU setups for large language models. This is the major driver of Nvidia’s profits last quater.


Jensen says that it’s taken Nvidia to develop its full hardware stack. He says nvidia is introducing a new computing model built on Nvidia’s accelerated computing paradigm.


Jensen is talking about the end of Moore’s Law, and how generative AI is an answer to the end of Moore’s Law. 


Jensen is talking about the IBM 360 from 1964, specifically the importance of the CPU. 


An example of Nvidia Ace in a game

(Image credit: Future / John Loeffler)

Nvidia Ace, a real time AI model rendering tool. This looks like something for procedurally-generated missions and content.

Jensen says that the characters in this game aren’t prescripted, and they have a typical quest giver NPC type. The conversation was a bit stilted, but not too bad. Maybe Oblivion level dialogue. Not bad for an AI.

Jensen showing off the RTX 4060 Ti GPU and a 14-inch laptop running Cyberpunk 2077 with real-time RT.


Jensen Huang on stage at Computex 2023

(Image credit: Future / John Loeffler)

And here’s Jensen!


Oh yeah, this event is going to be very AI heavy from this intro.


It looks like the event is about to start!


The stage at Nvidia's Computex 2023 keynote

(Image credit: Future / Mark Anthony Ramirez)

We’re all filing into our seats and waiting for the keynote to start, and the energy is great. It’s good to be back at Computex! We missed you Taipei! 


An Nvidia banner at Computex 2023

(Image credit: Future / John Loeffler)

I’m waiting to get into the Nvidia keynote event as we speak, which should be kicking off in the next 20 minutes. I’ll keep you posted on all the latest as everyone gets in and settled.


Howdy folks, this is John Loeffler, TechRadar’s Components Editor, and I am in Taipei after a grueling 15-hour flight from New York City, but I’m pumped to be here and bring you all the latest from Nvidia’s keynote, as well as all the rest of Computex 2023.

I can’t say for certain what we’ll see in a couple of hours, but I expect that it will be exciting, at least as far as AI is concerned, considering how much Nvidia’s GPU hardware is integral to these latest AI advances.

Will we get graphics card talk? I’m almost certain that it will come up, but will we get prices and release dates for the RTX 4060 Ti 16GB and RTX 4060? I sure hope so, but we’ll know in a couple of hours from now once the event kicks off.

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