At the beginning of the big bang of modern AI,
we recognized the need to create a new kind of computer for a new way of developing software.
Software will be written by software running on AI computers.
This new type of computer will need new chips, new system architecture,
new ways to network,
new software,
and new methodologies and tools.
We've invested billions into this intuition, and proven helpful to the industry.
It all comes together as DGX - a computer for AI.
We offer DGX as a fully integrated system, as well as offer the components to the industry to create differentiated options.
I am pleased to see so much AI research advancing because of DGX - top universities, research hospitals, telcos, banks,
consumer products companies, car makers,
and aerospace companies.
DGX help their AI researchers - whose expertise is rare, scarce, and their work strategic.
It is imperative to make sure they have the right instrument.
Simply, if software is to be written by computers, then companies with the best software engineers will also need the best computers.
We offer several configurations - all software compatible.
The DGX A100 is a building block that contains 5 petaflops of computing and super-fast storage and networking to feed it.
DGX Station is an data-center-in-a-box designed for workgroups - - plugs into a normal outlet.
And DGX SuperPOD is a fully integrated, fully network-optimized, AI-data-center-as-a-product.
SuperPOD is for intensive AI research and development.
NVIDIA's own new supercomputer, called Selene, is four SuperPODs -
it is the 5th fastest supercomputer and the fastest industrial in the world's TOP500.
In total, NVIDIA is operating an equivalent of 18 SuperPODs
We’ve invested a couple of billions of dollars in computing infrastructure to do basic research in AI,
to develop AI software that goes into our products,
and to develop our library of pre-trained models.
We have a new DGX Station 320G.
DGX Station can train large models –
320GB of super-fast HBM2e connected to four A100 GPUs
over 8TB per second of memory bandwidth.
8TB transferred in one second.
It would take CPU servers to achieve this memory bandwidth.
DGX Station plugs into a normal wall outlet, like a big gaming rig, consumes just 1,500 watts, and is liquid-cooled to a silent 37 db.
Take a look at the cinematic that our engineers and creative team did.
This is the processor board
and exactly the same one that goes into supercomputers.
This is a true server with baseboard management controller for remote management.
And it's heavy.
These are solid-copper special cold plates
with super fine-pitch fin stacks -
these plates brazed, joined to copper tubing in a hydrogen furnace to create flux-free joints.
This,
this right here,
this
connects to a phase-change refrigerant cooling system
DGX can store and release significant amounts of heat energy by changing phase from liquid to vapor and back.
Unlike typical liquid-cooling systems, DGX is closed-looped and requires no maintenance.
Imagine if Alex, Ilya, and Geoff Hinton had a DGX Station in 2012 when they won ImageNet.
Ten years ago, used a GeForce GTX 680 -
3 teraflops,
2GB frame buffer
at 150 gigabytes per second.
DGX Station 320G
is 400x the compute with Tensor Core,
100x the memory capacity with NVLink,
and 300x the bandwidth with HBM2e and 2.5D CoWoS packaging.
A CPU cluster of this performance would cost about a million dollars today.
DGX Station is $149,000 -
the ideal AI programming companion for every AI researcher.
Today we are also announcing a new DGX SuperPOD.
Three major upgrades:
The new 80GB A100, which brings the SuperPOD to 90TB of HBM2 memory,
with aggregate bandwidth
of 2.2 exabytes per second.
It would take 11,000 CPU servers to achieve this bandwidth -
about a 250-rack data center -
15x bigger than the SuperPOD.
Second,
SuperPOD has been upgraded with NVIDIA BlueField-2.
SuperPOD is now the world's first cloud-native supercomputer,
multi-tenant shareable,
with full isolation and bare-metal performance.
And third,
we're offering Base Command,
the DGX management and orchestration tool used within NVIDIA.
We use Base Command to support thousands of engineers,
over 200 teams, consuming a million-plus GPU-hours a week.
DGX SuperPOD starts at $7 million and scales to $60 million for a full system.
Are doing.