Nvidia Unveils Its First Ampere-Based GPU, Raises Bar for Data Center AI

In lieu of the multi-day extravaganza that is usually Nvidia’s flagship GTC in San Jose, the business has actually been rolling out a series of talks and announcementsonline Even the keynote has actually gone virtual, with Jensen’s popular and typically rambling talk being moved to YouTube. To be sincere, it’s really easier to cover keynotes from a livestream in an office anyhow, although I do miss out on all the hands-on demonstrations and mingling that goes with the in- individual conference.

In any case, this year’s event featured a remarkable suite of announcements around Nividia’s new Ampere architecture for both the data center and AI on the edge, start with the A100 Ampere-architecture GPU.

Nvidia A100 GPU

Nvidia A100: World’s Largest 7nm Chip Features 54 Billion Transistors

Nvidia’s first Ampere-based GPU, its new A100 is also the world’s biggest and most complicated 7nm chip, including an incredible 54 billion transistors. Nvidia claims performance gains of up to 20 x over previous Voltamodels The A100 isn’t just for AI, as Nvidia thinks it is a perfect GPGPU device for applications consisting of data analytics, clinical computing, and cloud graphics. For lighter-weight tasks like inferencing, a single A100 can be separated in up to 7 pieces to run several loads in parallel. On The Other Hand, NVLink allows several A100 s to be firmly paired.

All the leading cloud suppliers have actually stated they prepare to support the A100, consisting of Google, Amazon, Microsoft, and Baidu. Microsoft is already preparation to forge ahead of its Turing Natural Language Generation by moving to A100 s for training.

Innovative TF32 Aims to Enhance AI Performance

Along with the A100, Nvidia is rolling out a new type of single-precision floating-point-TF32 for the A100’s Tensor cores. It is a hybrid of FP16 and FP32 that intends to keep some of the performance advantages of moving to FP16 without losing as much accuracy. The A100’s new cores will also directly support FP64, making them progressively beneficial for a range of HPC applications. Along with a new data format, the A100 also supports sporadic matrices, so that AI networks which contain lots of un-important nodes can be more effectively represented.

Nvidia DGX A100: 5 PetaFLOPS in a Single Node

Nvidia DGX A100 Along with the A100, Nvidia announced its latest data center computer, the DGX A100, a significant upgrade to its current DGXmodels The first DGX A100 is already in use at the US Department of Energy’s Argonne National Laboratory to help with COVID-19research Each DGX A100 features 8 A100 GPUs, offering 156 TFLOPS of FP64 performance and 320 GB of GPUmemory It’s priced beginning at “only” (their words) $199,000 Mellanox interconnects allow for several GPU releases, however a single DGX A100 can also be separated in up to 56 circumstances to allow for running a number of smaller sized work.

In addition to its own DGX A100, Nvidia anticipates a number of its traditional partners, consisting of Atos, Supermicro, and Dell, to build the A100 into their own servers. To help in that effort, Nvidia is also offering the HGX A100 data center accelerator.

Nvidia HGX A100 Hyperscale Data Center Accelerator

Nvidia DGX A100 Internals The HGX A100 consists of the underlying building blocks of the DGX A100 supercomputer in a type element ideal for cloud release. Nvidia makes some extremely excellent claims for the price-performance and power effectiveness gets that its cloud partners can anticipate from moving to the new architecture. Particularly, with today’s DGX-1 Systems Nvidia says a common cloud cluster consists of 50 DGX-1 systems for training, 600 CPUs for reasoning, costs $11 million, inhabits 25 racks, and draws 630 kW ofpower With Ampere and the DGX A100, Nvidia says just one kind of computer is required, and a lot less of them: 5 DGX A100 systems for both training and reasoning at a cost of $1 million, inhabiting 1 rack, and taking in just 28 kW of power.

DGX A100 SuperPOD

Obviously, if you’re a hyperscale calculate center, you can never ever have adequate processorpower Nvidia has actually developed a SuperPOD from 140 DGX A100 systems, 170 InfiniBand switches, 280 TB/s network material (using 15 km of optical cable television), and 4PB of flash storage. Nvidia declares that all that hardware provides over 700 petaflops of AI performance and was built by Nvidia in under 3 weeks to use for its own internalresearch If you have the space and the money, Nvidia has actually launched the recommendation architecture for its SuperPOD, so you can build your own. Joel and I believe it sounds like the makings of a great DIY post. It should have the ability to run his Deep Space 9 upscaling project in about a minute.

Nvidia Broadens Its SaturnV Supercomputer

Obviously, Nvidia has also considerably broadened its SaturnV supercomputer to capitalize of Ampere. SaturnV was made up of 1800 DGX-1 Systems, however Nividia has now added 4 DGX A100 SuperPODs, bringing SaturnV to a declared total capability of 4.6 exaflops. According to Nvidia, that makes it the fastest AI supercomputer in theworld Nvidia also announced a high-powered, function-built GPU for edge computing. The Jetson EGX A100 is built around an A100, however also consists of Mellanox CX6 DX high-performance connection that’s protected using a line speed crypto engine. The GPU also consists of support for encrypted models to help safeguard an OEM’s intellectualproperty Updates to Nvidia’s Jetson-based toolkits for various markets (consisting of Clara, Jarvis, Aerial, Isaac, and Metropolitan area) will help OEMs build robotics, medical gadgets, and a range of other high-end items using the EGX A100

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