Top 5 Enterprise Use Cases for NVIDIA Hopper GPUs in AI and HPC

Keeping up with the modern enterprise environment is hard. You need to outpace, outscale and out-innovate. No matter if you are building advanced AI models or running intensive simulations, the pressure to deliver faster outcomes is the same.

The challenge? Legacy infrastructure cannot handle the scale or complexity that enterprise workloads now demand.

That’s when you know you need powerful infrastructure and compute to support these workloads at scale. For example, enterprises are achieving great results by using NVIDIA Hopper GPUs, which are specifically built to handle the most intensive AI and HPC workloads, thanks to their high-performance architecture.

Continue reading this blog as we’ll explore the top five enterprise use cases for NVIDIA Hopper GPUs.

Use Case 1: Large-Scale AI Training

Training massive AI models like Large Language Models (LLMs) or advanced vision models demands extraordinary compute power and efficiency. Enterprises engaged in AI R&D and production know that traditional GPU architectures are now inadequate for the scale and complexity of modern AI workloads.

The NVIDIA Hopper architecture was purpose-built to accelerate AI training at scale. For instance, Meta’s Llama 3, a groundbreaking 405 billion parameter LLM  was trained using over 16,000 NVIDIA Hopper H100 GPUs. This was the first time a Llama model was trained at this scale, showing how Hopper GPUs enable enterprises to push the boundaries of AI research. 

Also Read: How to Scale AI Training Like Meta: A Case Study

Use Case 2: AI Inference at Scale

Training models is only half the story. Deploying them efficiently in real-time applications is equally important. If you are powering recommendation engines, autonomous vehicles or fraud detection systems, you already know AI inference demands low latency, high throughput and reliability.

NVIDIA Hopper GPUs excel at large-scale inference with features like the Transformer Engine for accelerated sparse matrix operations. Combine it with advanced networking technologies such as NVIDIA Quantum-2 InfiniBand to ensure ultra-low latency and high throughput, ideal for enterprises processing thousands of real-time AI queries per second.

For example, retail enterprises deploying recommendation systems can serve personalised content to millions of users simultaneously without sacrificing response time or accuracy. 

Use Case 3: Scientific Simulations

From drug discovery pipelines to climate change modelling, scientific simulations require extreme compute resources and precision. The Hopper architecture supports double-precision (FP64) computations ideal for scientific accuracy, alongside mixed-precision capabilities that boost performance for less precision-sensitive calculations. Its large on-chip memory and high memory bandwidth accelerate complex simulations, so enterprises can reduce time-to-insight.

Pharmaceutical companies, for instance, can run molecular dynamics simulations faster and shorten drug development cycles. Environmental agencies can also perform high-resolution climate modelling with improved accuracy for better forecasting and policy-making.

Use Case 4: Financial Modelling & Risk Analysis

Financial enterprises operate in an environment of constant change where real-time analytics and risk evaluation underpin decision-making. The Hopper GPU’s architecture offers the compute to run complex simulations and AI-powered risk assessments at scale. Its high throughput and low latency are imperative for leading in trading environments where milliseconds matter.

Financial institutions can leverage NVIDIA Hopper GPUs to:

• Perform ultra-fast scenario analysis and stress testing.
• Detect fraud and market manipulation using AI models.
• Optimise asset allocations with reinforcement learning.

Use Case 5: Generative AI for Content Creation

Enterprises are now adopting Generative AI to create content across all departments, from marketing copy and code generation to synthetic media production. Hence, the demand for faster and scalable compute has never been higher.

Hopper GPUs with their Transformer Engine and advanced Tensor Cores are optimised for generative AI models such as GPT, Stable Diffusion and other multimodal architectures. Enterprises can train and fine-tune these models faster while deploying inference at scale for real-time content generation.

NVIDIA Hopper GPUs on the AI Supercloud

The NVIDIA Hopper architecture is purpose-built for the most demanding enterprise workloads at scale. But raw power is only part of the equation.

At the AI Supercloud, we don’t just provide access to NVIDIA Hopper GPUs, we optimise them to your unique needs. Here’s what you get when you deploy on the AI Supercloud:

Reference Architecture

For enterprises, deploying cutting-edge hardware is only as good as the underlying architecture that supports it. The AI Supercloud features industry-leading reference architecture co-developed with NVIDIA, including Hopper GPUs such as the NVIDIA HGX H100 and NVIDIA HGX H200.

You get:

• Minimised deployment risks to ensure reliability and stability for your mission-critical workloads
• Optimised high throughput and low latency performance for faster training of large AI models and complex simulations.
• Streamlined deployment that accelerates your time to value.

Customisation

No two enterprises have identical needs. If your workload demands ultra-fast GPUs, high CPU counts, expansive RAM or specialised storage solutions, customising your stack ensures you only pay for and use what you need.

You get:

• Cost savings by aligning compute resources exactly with your workload needs, preventing unnecessary spending on AI training or inference.
• Optimised performance by customising your infrastructure to suit the specific demands of your AI or HPC applications.
• Future-ready with easy upgrades and scaling of individual components as your enterprise and workloads grow.

Advanced Networking and Storage

High-speed and low-latency data movement is often the bottleneck in enterprise AI and HPC workflows. Our GPU clusters for AI and HPC are equipped with NVIDIA-certified WEKA storage with GPUDirect Storage and NVIDIA Quantum-2 InfiniBand for ultra-fast data movement and model training.

You get:

• Faster data pipelines to keep GPUs fully utilised by quickly feeding large datasets during training or simulations.
• Real-time inference for rapid data access and result delivery, ideal for financial risk analysis and recommendation engines.
• Seamless multi-GPU coordination with high-speed interconnects like NVLink and InfiniBand for smooth scaling of enterprise AI workloads.

Scalability

Enterprise workloads are dynamic, you may need to quickly ramp up GPU resources for a large training project or scale down during quieter periods. With Hyperstack, our on-demand GPUaaS platform, you can burst instantly with high-performance Hopper GPUs like NVIDIA H100 SXM or grow into thousands of Hopper GPUs within as little as 8 weeks on the AI Supercloud.

You get:

• Elastic resource scaling to handle peak demand efficiently without the cost of permanent infrastructure.
• Immediate access to compute resources for faster project starts and shorter innovation cycles.
• Confident scaling from pilot to production without delays or infrastructure limitations.

Sovereign AI Infrastructure

Considering the modern regulatory environment, enterprises must meet the data sovereignty and compliance standards. Running NVIDIA Hopper GPUs within a sovereign AI infrastructure ensures your data and AI workloads remain within your jurisdiction, aligning with local legal and security requirements. And this is exactly what you get at NexGen Cloud. Learn more here. 

Want to Get Started? Talk to a Solutions Engineer.

FAQs

What makes NVIDIA Hopper GPUs suitable for enterprise AI workloads?

NVIDIA Hopper GPUs offer high throughput, low latency and support for massive model training and inference with the Transformer Engine.

Can NVIDIA Hopper GPUs handle both AI and HPC tasks?

Yes, NVIDIA Hopper GPUs are optimised for large-scale AI and scientific computing with FP64 precision and advanced memory bandwidth.

How does the AI Supercloud enhance NVIDIA Hopper performance?

The AI Supercloud delivers reference architecture, advanced networking and storage solutions that offer full NVIDIA Hopper GPU potential for enterprises.

Is the AI Supercloud scalable for growing enterprise needs?

Yes. You can instantly burst or scale into thousands of GPUs to support projects from pilot to production.