Presented by Nvidia
The rise of agentic AI
Over a decade ago, “perceptive AI” gave us models that could find patterns or anomalies in data and make predictions. But that intelligence was anchored to answers that were already known. Is this a picture of a dog or a cat? Is that a pedestrian crossing the road in front of me?
With “generative AI”, we can create new, never before seen content, shaped by our prompting. Instead of being served an answer to a previously answered question, each one of us is creating new text, images, voice and video using all the unstructured data we can throw at these modern AI models.
“Agentic AI” promises “digital agents” that learn from us, and can perceive, reason problems out in multiple steps and then make autonomous decisions on our behalf. They can solve multilayered questions that require them to interact with many other agents, formulate answers and take actions. Consider forecasting agents in the supply chain predicting customer needs by engaging customer service agents, and then proactively adjusting warehouse stock by engaging inventory agents. Every knowledge worker will find themselves gaining these superhuman capabilities backed by a team of domain-specific task agent workers helping them tackle large complex jobs with less expended effort.
The growing “shadow AI” problem
However, the proliferation of generative, and soon agentic AI, presents a growing problem for IT teams. Maybe you’re familiar with “shadow IT,” where individual departments or users procure their own resources, without IT knowing. In today’s world we have “shadow AI,” and it’s hitting businesses on two fronts.
- Consumer-oriented AI apps are proliferating at a rapid pace, and many enterprise knowledge workers are using them1, feeding them potentially sensitive, intellectual property and customer data, often engaging with services that are not properly guardrailed2. This is creating a huge governance risk for most enterprises.
- Many developers are also standing up their own IT silos to support their projects. Most of these silos have little if any knowledge of each other’s work, and are ramping up operating expenses as they procure computing for short term projects, which then go underutilized or wasted, along with data silos that impede the flow of vital information between teams. And maybe worst of all, they’re losing the opportunity to learn from each other in terms of sharing expertise and best practices to efficiently deliver AI applications to production.
The AI factory — built on Blackwell-powered Nvidia DGX
Today’s enterprises create value through insights and answers driven by intelligence, setting them apart from their competitors. Just as past industrial revolutions transformed industries — think about steam, electricity, internet and later computer software — the age of AI heralds a new era where the production of intelligence is the core engine of every business. The ability to create this digitized intelligence on a large scale is driving the demand for a new type of factory. This “AI factory” is the next evolution of enterprise infrastructure.
Instead of coal, electricity or software (the fuels of factories past), AI factories manufacture AI models to:
- Reduce operational costs
- Analyze vast amount of data and drive innovation
- Foster scale with agility
- Enhance enterprise productivity
AI factories are now the essential infrastructure on which organizations can have their own AI “center of excellence” — namely a unified platform on which people, process and infrastructure can be consolidated to gain key benefits including:
- Scaling AI talent, with citizen data science expertise groomed from within instead of hired from outside
- Standardization of tools and best practices that create an application development flywheel
- Maximized utilization of accelerated computing infrastructure that is centrally orchestrated
To enable the age of large language models (LLMs), agentic AI and what comes next, we’ve created the Nvidia DGX platform to be the engine that powers AI factories. Businesses have begun building their platforms with it, to enable leading-edge applications requiring many different expert models to work in concert with imperceivable latency, solving complex, multi-layered problems.
GPU-driven Nvidia DGXTM systems with Intel® Xeon® CPUs integrate Nvidia Blackwell accelerators with a next-generation architecture optimized for the era of agentic AI, while providing fifteen times greater inference throughput with twelve times greater energy efficiency3. This platform includes best-of-breed developer and infrastructure management software that streamlines and accelerates the application development lifecycle from development to deployment, while supporting ongoing model fine-tuning.
Real world impact now, not later
In an Nvidia analysis of AI factory implementers, we found many derived benefits that can counter the impact of shadow AI, improving time to market, productivity and infrastructure utilization — while enabling support for the rising tide of generative and agentic AI. These organizations shared the following benefits4, as expressed by Nvidia DGX platform customers:
- 6X increase in infrastructure performance compared with legacy IT infrastructure
- 20% greater productivity for data scientists and AI practitioners
- 90% infrastructure utilization
Typically, these benefits have been confined to hyperscalers who have decades of experience with operating high-performance infrastructure, along with a deep bench of expertise in running such platforms. The reality is that even the “experts” admit that their own platforms often can’t deliver the efficiencies needed, with many accepting 20-30% as the typical utilization factor4 of their infrastructure.
Now every business has the opportunity to have a hyperscale-class platform for their own AI factory, that’s easier to acquire, dramatically more efficient, simpler to manage and delivering benefits to the business now, not later.
Learn how to achieve AI-powered insights faster on GPU-driven Nvidia DGX™ systems, powered by Nvidia Tensor Core GPUs and Intel® Xeon® processors.
Tony Paikeday is Senior Director of Product Marketing, Artificial Intelligence Systems at Nvidia.
1. “Why IT leaders should seize on shadow AI to modernize governance” Dell Technologies / VentureBeat, Dec 2023
2. ”Generative AI: From Buzz to Business Value”, KPMG, June 2023
3. NVIDIA test comparisons from www.nvidia.com/dgx-b200: 32,768 GPU scale, 4,096x eight-way DGX H100 air-cooled cluster: 400G IB network, 4,096x 8-way DGX B200 air-cooled cluster: 400G IB network. Projected performance subject to change.
4. Chowdery, Aakanksha, et al., “PaLM: Scaling Language Modeling with Pathways,” arXiv, October 2022.
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