H100: NVIDIA’s Next-Gen GPU for AI and high-performance computing

Artificial Intelligence (AI) and High-Performance Computing (HPC) are redefining what’s possible for enterprises today, from accelerating innovation to transforming decision-making at every level. As models become larger and more complex, the demand for faster, more efficient, and cost-effective infrastructure grows stronger. This is where the NVIDIA H100 GPU stands out,  a next-generation accelerator designed to power the world’s most demanding AI and HPC workloads.

When combined with Tata Communications’ BareMetal GPU-as-a-Service and global cloud fabric, enterprises can unleash the full potential of NVIDIA H100 performance, with the added benefit of predictable costs, high security, and effortless scalability.

NVIDIA H100: Powering the next generation of enterprise AI workloads

The NVIDIA H100, part of the Hopper architecture, is built specifically for large-scale AI training, inference, and high-performance computing tasks. It’s the evolution of NVIDIA’s industry-leading A100, offering remarkable leaps in performance, efficiency, and capability.

For enterprises embracing AI transformation, the H100 GPU is a true game-changer. Its design enables organisations to handle massive workloads,  from training Large Language Models (LLMs) to accelerating real-time analytics, simulation, and research. In fact, when used through Tata Communications’ BareMetal GPU platform, businesses can access these GPUs on demand, without having to invest heavily in on-premises infrastructure.

Key benefits of NVIDIA H100 GPUs for enterprises include:

• Lightning-fast AI training: With support for FP8 precision, transformer engine optimisation, and improved tensor performance, training large models is significantly faster.
• Seamless scalability: The H100 GPU supports high-speed interconnects like NVLink and Infiniband, ideal for multi-node AI clusters.
• Energy efficiency and performance: The Hopper architecture offers greater performance per watt, reducing operational costs.
• Security and reliability: Integrated security features ensure enterprise-grade data protection, especially when combined with Tata Communications’ secure cloud fabric.

Architecture and performance innovations in the H100 GPU

The NVIDIA H100 chip represents a breakthrough in AI and HPC design. Built using the Hopper GPU architecture, it delivers massive computational gains and introduces new features that make it the most powerful NVIDIA GPU to date.

1. Hopper Architecture: Built for AI and data acceleration

The H100 NVIDIA GPU features the new Hopper architecture, which introduces key technologies designed specifically for AI training and inference. These include:

• Transformer engine: A new FP8 precision format that speeds up transformer-based models used in natural language processing (NLP) and generative AI.
• Fourth-Generation tensor cores: Deliver up to 4x faster performance than previous generations.
• NVLink and NVSwitch: Enable GPUs to communicate at ultra-high bandwidth, making distributed AI workloads more efficient.

2. Improved memory and bandwidth

The H100 GPU supports up to 80GB of HBM3 memory with a bandwidth of over 3TB/s. This means faster data access for large datasets, model checkpoints, and embeddings — crucial for LLMs and HPC simulations.

3. Secure, multi-Instance GPU (MIG) capabilities

H100 GPUs can be partitioned securely into multiple instances, allowing multiple users or workloads to share the same GPU while maintaining strict isolation. This flexibility is perfect for enterprises using shared environments or hybrid cloud setups.

Leveraging H100 GPUs for enterprise AI and HPC applications

The true strength of the NVIDIA H100 lies in its ability to handle real-world enterprise use cases from generative AI to complex scientific simulations. Through Tata Communications’ BareMetal GPU-as-a-Service, organisations gain access to H100 GPUs in a fully managed, scalable, and secure environment.

1. AI training and fine-tuning

The NVIDIA H100 GPU accelerates the training of large and multi-modal models. When paired with Tata Communications’ Infiniband network and high-speed parallel file systems (Lustre protocol), data transfer bottlenecks are eliminated, reducing training time dramatically.

2. Large-scale inference and production

For AI models deployed at scale, such as chatbots, recommendation engines, or generative AI assistants, the H100 NVIDIA GPU ensures low-latency, high-throughput inference, even for complex multi-modal workloads.

3. Scientific and financial modelling

HPC workloads like climate modelling, molecular simulation, and financial risk analysis demand immense computing power. The H100 chip delivers unmatched acceleration for these critical workloads.

4. MLOps and AI lifecycle management

Through Tata Communications’ AI Cloud ecosystem, enterprises can use MLOps tools, AI Studio, and Data Management for AI to streamline the model lifecycle,  from data preparation to continuous deployment, all optimised for H100 GPUs.

Pricing, deployment, and cost considerations for enterprises

Adopting advanced GPUs like the NVIDIA H100 can transform performance, but managing cost and predictability is equally important. Tata Communications helps enterprises strike the right balance through flexible deployment and transparent pricing models.

NVIDIA H100 price and cost insights

The NVIDIA H100 price varies depending on configuration, usage model, and deployment. Individual H100 GPUs can cost from tens of thousands of pounds, while complete DGX H100 systems, featuring multiple GPUs and integrated networking, are priced higher for full enterprise integration.

However, through Tata Communications’ BareMetal GPU-as-a-Service, businesses avoid large capital expenses by paying only for what they use. Options include:

• On-demand pricing: Pay hourly without long-term commitments.
• Reserved instances: Commit for a longer term to enjoy lower rates and predictable costs.
• Committed use discounts: Significant savings for consistent usage patterns.

Comparing H100 pricing across DGX systems and GPUs

The NVIDIA DGX H100 system combines eight H100 GPUs interconnected with NVLink and NVSwitch technology. Compared to standalone GPU deployment, the DGX H100 delivers superior scaling for enterprise AI workloads, though at a higher upfront cost. Tata Communications enables access to DGX-class performance without the investment in physical hardware, through its AI Cloud Infrastructure.

Key factors influencing H100 TCO

Total Cost of Ownership (TCO) for H100 GPUs depends on multiple factors:

1. Duration of use: Long-term commitments reduce per-hour cost.
2. Scale of deployment: Larger clusters benefit from volume pricing.
3. Data transfer and storage: Efficient data management systems, such as Tata Communications’ high-speed parallel file systems, help reduce overall costs.
4. Energy efficiency: The H100 chip offers better performance per watt, lowering power and cooling costs.
5. Support and management: Managed services simplify operations and further improve cost predictability.

Final thoughts on NVIDIA H100

The NVIDIA H100 GPU is more than just a next-generation processor. It's a gateway to enterprise-scale AI innovation. When combined with Tata Communications’  GPU Solution having secure, scalable, and cost-predictable infrastructure, enterprises can achieve true AI transformation, faster, smarter, and more affordably.

Whether it’s accelerating generative AI models, driving HPC research, or modernising analytics platforms, the H100 NVIDIA GPU, powered by Tata Communications’ BareMetal GPU service, offers the perfect blend of performance and predictability.

Ready to experience the full power of NVIDIA H100 with Tata Communications’ AI cloud fabric? schedule a conversation with our experts to design a cost-efficient, secure, and scalable AI infrastructure for your enterprise.

FAQs on NVIDIA H100 GPU and its role in enterprise AI

1. What makes the NVIDIA H100 GPU ideal for AI and high-performance computing workloads?

The H100 GPU offers advanced architecture, FP8 precision, and ultra-fast interconnects, delivering unmatched performance for AI training, inference, and scientific computing.

2. How does the DGX H100 system leverage H100 GPUs for enterprise applications?

The NVIDIA DGX H100 system integrates eight H100 GPUs with NVSwitch, enabling enterprise-grade scalability for large models and parallel computing tasks.

3. What factors should businesses consider regarding H100 pricing and total cost of ownership?

Enterprises should consider deployment type, usage duration, data transfer costs, and energy efficiency. Tata Communications offers predictable billing models, ensuring lower TCO without compromising performance.