Modern data centers host diverse applications that require varying system architectures.
Enterprise servers are optimized for a balance of strong single-threaded performance
and a nominal number of cores.
Hyperscale servers,
optimized for microservice containers,
are designed for a high number of cores, low cost, and great energy efficiency.
Storage servers are optimized for large number of cores and high I/O throughput.
Deep learning training servers are built like supercomputers
with the largest number of fast CPU cores,
the fastest memory, the fastest I/O,
and high-speed links to connect the GPUs.
Deep learning inference servers are optimized for energy efficiency
and best ability to process a large number of models concurrently.
And there are so many more -
converged, telco, industrial edge.
The genius of the x86 server architecture
is the ability to do a good job using varying configurations
of CPU, memory,
PCI express, and peripherals to serve all of these applications.
Yet processing large amounts of data remains a challenge for computer systems today -
this is particularly true for AI models like transformers and recommender systems.
Let me illustrate the bottleneck with half of a DGX.
Each Ampere GPU is connected to 80GB of super-fast memory running at 2 TB/sec.
Together, the four Amperes process 320GB at 8 TB/sec.
Contrast that with CPU memory, which is 1TB large,
but only 0.2 TB/sec, as opposed to 8 TB/sec.
The CPU memory is 3x larger but 40x slower than the GPU.
We would love to utilize the full 1,320GB of memory of this node to train AI models.
So, why not something like this?
Make faster CPU memories,
connect four channels to the CPU,
a dedicated channel to feed each GPU.
Even if a package can be made, PCI-Express is now the bottleneck.
We can surely use NVLink. NVLink is fast enough. But no x86 CPU has NVLink, not to mention four NVLinks.
Today, we're announcing our first data center CPU, Project Grace,
named after Grace Hopper, a computer scientist and U.S. Navy Rear Admiral,
who in the '50s pioneered computer programming.
Grace is Arm based and purpose built for accelerated computing applications of large amounts of data - such as AI.
Grace highlights the beauty of Arm.
Their IP model allowed us to create the optimal CPU for this application,
which achieves X-factors speed-up.
The Arm core in Grace is a next-generation, off-the-shelf IP for servers.
Each CPU will deliver 300 SPECint
with a total
of over 2,400 SPECint rate CPU performance for an eight-GPU DGX.
For comparison,
today’s DGX, the highest performance computer in the world
is 450 SPECint rate.
2,400 SPECint rate with Grace
versus 450 SPECint rate today.
So look at this again -
before,
after,
before,
after.
Amazing increase in system and memory bandwidth.
Today, we're introducing a new kind of computer.
The basic building block of the modern data center.
Here it is.
What I'm about to show you brings together the latest GPU-accelerated computing,
Mellanox high-performance networking,
and something brand new.
The final piece of the puzzle.
The world's first CPU designed for terabyte-scale accelerated computing.
Her secret codename:
Grace.
This powerful, Arm-based CPU gives us the third foundational technology for computing,
and the ability to rearchitect every aspect of the data center for AI.
We're thrilled to announce the Swiss National Supercomputing Center
will build a supercomputer powered by Grace
and our next-generation GPU.
This new supercomputer, called Alps,
will be 20 exaflops for AI,
10x faster,
than the world's fastest supercomputer today.
Alps will be used to do whole-earth-scale weather and climate simulation,
quantum chemistry, and quantum physics for the Large Hadron Collider.
Alps will be built by HP Enterprise
and come online in 2023.
We're thrilled by the enthusiasm of the supercomputing community,
welcoming us to make Arm a top-notch scientific computing platform.
Our data center roadmap is now a rhythm consisting of three chips:
CPU, GPU, and DPU.
Each chip architecture has a two-year rhythm
with likely a kicker in between.
One year will focus on x86 platforms.
One year will focus on Arm platforms.
Every year will see new exciting products from us.
The NVIDIA architecture and platforms will support x86 and Arm -
whatever customers and markets prefer.
Three chips; yearly leaps; one architecture.
Arm is the most popular CPU in the world.
For good reason -
it's super energy efficient.
Its open licensing model inspires a world of innovators to create products around it.
Arm is used broadly in mobile and embedded today.
For other markets like the cloud,
enterprise and edge data centers, supercomputing, and PCs,
Arm is just starting and has great growth opportunities.
Each market has different applications and has unique systems, software, peripherals, and ecosystems.
For the markets we serve, we can accelerate Arm's adoption.
Let's start with the big one - cloud.
One of the earliest designers of Arm CPUs for data centers is AWS.
Its Graviton CPUs are extremely impressive.
Today, we're announcing NVIDIA and AWS
are partnering to bring Graviton2
and NVIDIA GPUs together.
This partnership brings Arm into the most demanding cloud workloads -
AI and cloud gaming.
Mobile gaming is growing fast
and is the primary form of gaming in some markets.
With AWS-designed Graviton2,
users can stream Arm-based applications and Android games
straight from AWS.
It's expected later this year.
We’re announcing a partnership with Ampere Computing
to create a scientific and cloud computing SDK and reference system.
Ampere Computing's Altra CPU is excellent -
80 cores,
285 SPECint17,
right up there with the highest performance x86.
We are seeing excellent reception at supercomputing centers around the world and at Android cloud gaming services.
We are also announcing a partnership with Marvell
to create an edge and enterprise computing SDK and reference system.
Marvell Octeon excels at I/O, storage, and 5G processing.
This system is ideal for hyperconverged edge servers.
We're announcing a partnership with MediaTek
to create a reference system and SDK for Chrome OS and Linux PCs.
MediaTek is the world's largest SoC maker.
Combining NVIDIA GPUs and MediaTek SoCs will make excellent PCs and notebooks.