GTC 2022: Nvidia flexes its GPU and platform muscle groups

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Nvidia packed about three years’ price of stories into its GPU Know-how Convention at present.

Flamboyant CEO Jensen Huang’s 1 hour, 39-minute keynote coated plenty of floor, however the unifying themes to nearly all of the 2 dozen bulletins have been GPU-centered and Nvidia’s platform method to all the things it builds. 

Most individuals know Nvidia because the world’s largest producer of a graphics processing unit, or GPU. The GPU is a chip that was first used to speed up graphics in gaming programs. Since then, the corporate has steadily discovered new use instances for the GPU, together with autonomous automobiles, synthetic intelligence (AI), 3D video rendering, genomics, digital twins and lots of others. 

The corporate has superior so removed from mere chip design and manufacturing that Huang summarized his firm’s Omniverse growth platform as “the brand new engine for the world’s AI infrastructure.” 

In contrast to all different silicon producers, Nvidia delivers its product as greater than only a chip. It takes  a platform method and designs full, optimized options which can be packaged as reference architectures for its companions to then construct in quantity. 

This 2022 GTC keynote had many examples of this method.

NVIDIA Hopper H100 Techniques ‘rework’ AI 

As famous earlier, the core of all Nvidia options is the GPU and at GTC22, the corporate introduced its new Hopper H100 chip, which makes use of a brand new structure designed to be the engine for massively scalable AI infrastructure. The silicon contains a whopping 80B transistors and features a new engine, particularly designed for coaching and inferencing of transformer engines. For these with solely a cursory information of AI, a transformer is a neural community that actually transforms AI primarily based on an idea referred to as “consideration.” 

Consideration is the place each component in a chunk of knowledge tries to determine how a lot it understands or must find out about different elements of the info. Conventional neural networks take a look at neighboring knowledge, whereas transformers see the complete physique of knowledge. Transformers are used extensively in pure language processing (NLP), since finishing a sentence and understanding what the following phrase within the sentence ought to be – or what a pronoun would imply – is all about understanding what different phrases are used and what sentence construction the mannequin may have to be taught. 

The chip alone gives huge processing functionality, however a number of GPUs might be linked collectively utilizing Nvidia’s NVLink interconnect, successfully creating one massive GPU leading to 4.9 Tbps of exterior bandwidth. 

On a associated word, Huang additionally introduced an growth of NVLink from an inside interconnect know-how to a full exterior swap. Beforehand, NVLink was used to attach GPUs inside a computing system. The brand new NVLink swap allows as much as 256 GPUs to behave as a single chip. The aptitude to go exterior the system leads to compute efficiency of 192 Teraflops. Whereas this may appear to be a loopy quantity of efficiency, recommender programs, natural-language processing and different AI use instances are ingesting huge quantities of knowledge, and these knowledge units are solely getting bigger on a regular basis. 

Persevering with with the platform theme, Nvidia additionally introduced new DGX H100-based programs, SuperPODs (multi-node programs) and a 576-node supercomputer. This can be a turnkey system with all of the software program and {hardware} required for near- plug-and-play AI duties. Like all its programs, that is constructed as a reference structure with manufacturing programs out there from a variety of system suppliers, together with Atos, Cisco, Dell, HPE, Lenovo and different companions.

AI Enterprise 2.0 is now full stack  

There could also be no higher instance of the platform method than how Nvidia has enabled enterprise AI. The corporate approaches this phase with a multi-layer mannequin. The underside layer is the AI infrastructure, which incorporates totally different programs akin to DGX, HGX, EGX and others constructed on NVIDIA’s big selection of GPUs and DPUs. Above that, Nvidia gives all the mandatory software program and working programs to let builders work with the {hardware}. This consists of CUDA, TAO, RAPIDS, Triton Inference Server, TensorFlow and different software program. 

The highest layer is a set of pre-built AI programs to handle particular use instances. For instance, Maxine is the corporate’s video AI system, Clara is designed for healthcare, Drive for the auto business and Isaac is its simulator. 

This allows enterprises and software program distributors to make use of these elements to ship revolutionary new capabilities. For instance, unified communications vendor, Avaya, makes use of Maxine in its Areas product for noise removing, digital backgrounds, and different options in video conferences. Most of the auto producers together with Jaguar and Mercedes are utilizing Drive because the platform for autonomous automobiles. 

Huang additionally introduced the formalization of the AI platform. When one thinks of different enterprise platforms, akin to VMware vSphere and Home windows Servers, these have a steady innovation roadmap and an ecosystem of validated software program that runs on them. NVIDIA presently has a program for the underlying {hardware} with distributors that embody Lenovo, Dell and Cisco. The corporate is complementing this with a software program program referred to as Nvidia Speed up, which presently has greater than 100 members, together with Adobe and Keysight. This could give clients the arrogance that the software program has been examined, vetted and optimized for the Nvidia platform. 

Omniverse expands to the clouds 

Nvidia’s Omniverse is a collaboration and simulation engine that obeys all of the legal guidelines of physics. Firms can use this to construct a digital model of an object chopping down coaching time. For instance, educating a robotic to stroll might be costly and time-consuming, as a result of one would want to construct quite a lot of situations akin to uphill, downhill, stairs and extra. With Omniverse, this may be finished nearly, the info uploaded, and the robotic then has the potential of strolling instantly.  One other use case is to construct digital twins of one thing like a manufacturing unit so constructing planners can design it to scale earlier than development begins. 

At GTC22, Nvidia introduced Omniverse Cloud, which because the title suggests, makes the simulation engine out there as a streaming cloud service. Traditionally, one would want a high-powered system to run Omniverse. Now as a cloud service, it may well run on any computing system, even a Chromebook or pill. This democratizes Omniverse and makes it out there to anybody with an Web connection.

The second announcement is the OVX Computing System, which is a knowledge center-scale system for industrial digital twins. The system begins with eight NVIDIA A40 GPUs and scales up from there. Once more, like all of its programs, this can be a reference structure with programs coming from Lenovo, Inspur and Supermicro.

Platform method has created sustainable differentiation 

Many business watchers have been predicting Nvidia ‘s dominance in GPUs to come back to an finish as extra silicon producers enter the market, which creates competitors and pricing strain. For instance, Intel has been aggressively pursuing GPUs for years, however nobody has managed to make a dent in Nvidia’s enterprise. The platform method Nvidia has taken is widespread in networking, cloud and software program however is exclusive to it in silicon. The benefits have been highlighted in Jensen’s keynote and have created long-term differentiation for the corporate.


Chris Preimesberger
is a former editor of eWEEK and an everyday VentureBeat contributor who has been reporting on and analyzing IT tendencies and merchandise for greater than twenty years.

Zeus Kerravala is the founder and principal analyst with ZK Analysis. He spent 10 years at Yankee Group and previous to that held quite a lot of company IT positions. Kerravala is taken into account one of many prime 10 IT analysts on the planet by Apollo Analysis, which evaluated 3,960 know-how analysts and their particular person press protection metrics.

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