Artificial Intelligence is revolutionizing the world we live in at an incredible pace, introducing new opportunities to boost efficiency, productivity, and creativity in every industry. But powering the latest AI models and ecosystems isn’t easy. That’s where NVIDIA GPUs for AI come in.
Traditional Central Processing Units (CPUs), designed for sequential task execution, struggle to cope with the demands of complex AI workloads. GPUs designed for AI, like the cutting-edge models from NVIDIA, excel in tasks like parallel processing, making them perfect for AI applications.
- By accelerating model training, real-time decision-making, and more, all while reducing operational costs, NVIDIA GPUs are becoming indispensable to enterprises looking to harness the full power of artificial intelligence. Learn more about NVIDIA’s enterprise AI solutions here, or discover how to optimize your NVIDIA AI investment with our top tips here.
While various other companies have experimented with building AI-ready GPUs, NVIDIA is in charge, constantly introducing new chips and systems tuned to the latest AI revolutions. Here’s how NVIDIA GPUs for AI are reshaping the enterprise landscape.
NVIDIA GPUs for AI: Overcoming the Limitations of CPUs
The capabilities of any AI solution are dependent on various things, from access to significant computing power to consistent training. Building, deploying, and maintaining an AI system all starts with an investment in the right hardware. Without the right “chips” we wouldn’t have the AI solutions we use today, from ChatGPT, to Midjourney.
CPUs, engineered for sequential processing, are excellent at executing a series of instructions in a linear format – making them fantastic for general-purpose computing. But CPUs struggle to power AI applications, which require systems to process huge volumes of data simultaneously.
That’s where GPUs make all the difference. They’re designed with smaller cores capable of handling thousands of operations simultaneously. This makes them more effective at handling repetitive, data-intensive tasks daily in AI workloads.
NVIDIA’s GPUs, specially designed for AI applications, build on the unique capabilities of standard GPU systems with specialized components, such as Tensor cores, which accelerate training and inference phases in AI development. For example, the NVIDIA A100 Tensor core can deliver up to 20 times higher performance than previous competitors.
How NVIDIA GPUs for AI Supercharge Performance
NVIDIA didn’t just change the computing world by introducing the world’s first generally commercially available GPU. The company continued to innovate, designing specific GPUs for unique AI use cases. For instance, some of the most popular NVIDIA GPUs for AI include:
- H100 GPUs: Built on the Hopper architecture, H100 GPUs are some of the most powerful enterprise GPUs engineered for AI training, inference, and high-performance computing workloads. These GPUs feature 14,592 CUDA cores, 80 GB of HBM3 memory, and a staggering 35 TB/s memory bandwidth. With Transformer Engine capabilities, the H100 accelerates large language models (LLMs) and generative AI applications.
- A100 GPUs: Optimized for data center AI, A100 GPUs are versatile accelerators supporting deep learning, analytics, and scientific computing applications. They feature 6,912 CUDA cores, 80 GB of HBM2e memory, and 2 TB/s memory bandwidth. The A100 is widely deployed across enterprise AI, enabling efficient multi-instance GPU (MIG) partitioning for cloud-based AI workloads.
- Blackwell GPUs: NVIDIA’s next-gen Blackwell architecture GPUs are built on a custom TSMC 4NP process, packing 208 billion transistors. Fifth-generation Tensor Cores introduce new FP4 capabilities, doubling AI throughput while halving memory requirements. Plus, they support the second-gen FP8 Transformer Engine to accelerate deep learning tasks.
The Impressive Capabilities of NVIDIA GPUs
Beyond being fast, accurate, and powerful, NVIDIA GPUs for AI come packed with unique components designed specifically for AI tasks. For instance, the Tensor cores built into NVIDIA’s GPUs optimize matrix multiplications, which are valuable in deep learning training and help to ensure faster processing with less power.
NVIDIA’s high memory bandwidth and capacity mean they can manage large datasets easily and execute complex tasks with AI models. Additionally, NVIDIA’s GPUs provide a platform for parallel computing alongside access to architectural solutions, like CUDA, which enables developers to harness the power of their GPUs for various applications.
How Businesses Benefit from NVIDIA AI GPUs
Combined with cutting-edge software, NVIDIA GPUs for AI are changing the game for both enterprises and leading AI companies, such as Anthropic, Microsoft, and Google. With NVIDIA’s cutting-edge hardware, organizations benefit from:
- Accelerated AI Model Training and Deployment: Training AI models – particularly cutting-edge LLMs and advanced systems, takes a lot of work. CPUs just can’t keep up with demand, but NVIDIA’s GPUs, with their parallel processing abilities, seriously reduce training times, ensuring faster deployment and iteration.
- Real-time analytics and decision-making: In every industry, the ability to quickly analyze and make decisions based on data is crucial. NVIDIA’s intelligent systems make it easier to process large amounts of data rapidly to allow for faster, more accurate decision-making across industries from healthcare to finance.
- Cost reductions: Since NVIDIA’s GPUs for AI are designed to perform technical tasks faster and with greater energy efficiency than CPUs, they can help reduce operational costs. That’s particularly valuable for large-scale AI deployment strategies.
How Nvidia GPUs for AI Are Transforming Key Sectors
So, how exactly are NVIDIA GPUs for AI influencing the growth of companies investing in AI across sectors? Let’s take a look at some real-world case studies.
Healthcare: AI-Powered Medical Imaging and Drug Discovery
In healthcare, companies like Terray Therapeutics use NVIDIA GPUs and the NVIDIA DGX Cloud to train foundation models for chemistry and generative AI. These technologies are helping the company to rapidly enhance the development of small-molecule therapeutics, unlocking new treatment opportunities for patients worldwide.
Autonomous Vehicles: Powering Self-Driving Innovations
In the automotive industry, NVIDIA GPUs for AI, as well as cutting-edge software solutions, power systems like “NVIDIA DRIVE AGX”. This end-to-end platform uses GPUs to process huge volumes of data in real-time, helping autonomous vehicles to navigate environments safely. Companies like Hyundai and Toyota are already building autonomous cars with this platform.
Technology: Developing AI Models Faster
Perplexity, a leader in the AI landscape, is making it easier for developers to integrate innovative open-source large language models into their projects with the “PPLX-API.” This API tool, powered by NVIDIA GPUs, is optimized for fast interference with NVIDIA TensorRT. With NVIDIA, Perplexity ensures companies can scale and optimize their AI strategies fast.
Finance: Enhancing Fraud Detection
The finance sector uses NVIDIA GPUs to power AI algorithms to detect fraudulent activities quickly and accurately. American Express, for instance, has trained several models using NVIDIA’s AI GPUs and software solutions, achieving a 50x improvement over their previous CPU-based configuration.
Telecom & 5G: AI-Driven Enterprise Services
In the telecoms sector, NVIDIA GPUs empower companies to deliver intelligent connectivity services to enterprises. For instance, Singtel developed an MEC platform with NVIDIA GPUs to help companies innovate with business-critical AI applications, real-time video analytics, chatbots, AR, and VR technologies.
Why Businesses Should Invest in Nvidia GPUs
NVIDIA GPUs for AI are quickly becoming the heart of the rapid growth of AI adoption worldwide, and they’re only becoming more advanced. Every year, NVIDIA plans to release new intelligent GPUs and chips for their enterprise customers, building on their impressive Blackwell architecture and existing enterprise-ready tools.
