What is the Nvidia H100 Tensor Core GPU?

The Nvidia H100 is Nvidia’s flagship data center GPU built on the Hopper architecture (launched in 2022). It’s the successor to the A100 (Ampere architecture) and represents a significant leap in performance and capabilities for artificial intelligence and high-performance computing. The H100 is a Tensor Core GPU optimized for massive parallel workloads – think training huge neural networks, running large language models (LLMs), and crunching scientific simulations that were previously impractical. In short, if the A100 was the workhorse of the AI world in 2020, the H100 is the new thoroughbred for 2023 and beyond, delivering unprecedented speed and efficiency.

NVIDIA H100 “Hopper” GPU module. The H100 packs 80 billion transistors and 80GB of ultra-fast HBM3 memory (visible as modules surrounding the chip) to feed its 18,432 CUDA cores. This powerhouse requires robust cooling and power (≈700W TDP) but delivers record-breaking performance for AI and HPC workloads.

As the first GPU based on Hopper, the H100 introduced several breakthrough innovations over its predecessor:

• Fourth-Generation Tensor Cores with FP8 Precision: Hopper adds support for 8-bit floating point (FP8) data via its new Transformer Engine. This allows much faster matrix math operations for AI. In fact, NVIDIA’s benchmarks show H100 can achieve up to 9× faster training and 30× faster inference on large language models compared to the A100 . These Tensor Cores also still support FP16, BF16, INT8, and other precisions with improved throughput, plus the fine-grained sparsity feature to double effective performance on supported models.
• Massive Memory Bandwidth: The H100 comes with 80 GB of HBM3 memory (High Bandwidth Memory), delivering over 3 TB/s of memory bandwidth (about a 67% increase over the A100’s HBM2e) for feeding data-hungry workloads. This means training ultra-large models or processing big scientific datasets is smoother and faster, with less bottlenecking on data transfer.
• High-Speed Interconnects (NVLink 4 and PCIe Gen5): H100 GPUs can communicate with each other and with host CPUs faster than ever. NVLink 4.0 provides up to 900 GB/s GPU-to-GPU bandwidth (50% higher than A100’s NVLink 3), crucial for multi-GPU training across nodes. Additionally, support for PCIe Gen5 doubles the host interface speed, which is great for cloud and on-prem systems that leverage the latest servers.
• New DPX Instructions for HPC: Hopper introduced DPX instructions that accelerate dynamic programming algorithms by up to 7× (e.g. speeding up DNA sequence alignment and route optimization). Combined with 3× higher FP64 performance (double-precision) versus A100, the H100 greatly benefits scientific computing tasks in fields like genomics, fluid dynamics, and climate modeling.
• Multi-Instance GPU (MIG) Enhancements: Like the A100, the H100 supports MIG virtualization, allowing the GPU to be partitioned into as many as seven isolated instances. This is especially useful in cloud environments – a single H100 can be split to run multiple smaller workloads securely, maximizing utilization. For example, you could have one H100 serving several different AI inference jobs in parallel, each with its own allocated share of memory and cores.

Nvidia H100 vs A100: How Much Better Is H100?

It’s clear that the H100 is a major generational upgrade over the A100. But just how much better is it? Let’s compare some core specs and features of H100 (Hopper) vs A100 (Ampere):

• Compute Cores and Performance: The H100 packs 18,432 CUDA cores vs. 6,912 in the A100 – over 2.5× more. Its theoretical peak throughput is about 60 TFLOPS (FP32), roughly 3× the A100’s ~20 TFLOPS. In practical terms, for most AI tasks the H100 delivers 2–3× faster performance generation-over-generation. Certain tasks see even bigger gains; for example, thanks to FP8 Tensor Cores, training large transformers can be 6–9× faster on H100 than A100 in optimized scenarios.
• Memory and Bandwidth: Both GPUs offer up to 80GB VRAM, but the H100’s HBM3 memory is significantly faster (≈3.35 TB/s vs 2.0 TB/s on A100). This 67% jump in memory bandwidth means the H100 can keep its cores fed with data more efficiently, which is crucial for large models and HPC apps that stream tons of data.
• New Features: The H100 introduces new capabilities the A100 lacks, such as the Transformer Engine for automatic mixed precision (enabling those FP8 speedups) and DPX instructions for specialized acceleration (A100 had none of these). The interconnect bandwidth improvements (NVLink 4, PCIe 5.0) also give H100 an edge in multi-GPU scaling and I/O. Essentially, Hopper was designed with next-generation AI workloads in mind (like giant language models and recommendation systems) and with features to accelerate them beyond what Ampere could do.
• Power Consumption: One trade-off is power – the H100 has a ~700W TDP (SXM5 form factor) versus 400W for A100. It runs hotter and demands more robust cooling. This means on-premise deployments need to account for higher energy and cooling costs. However, the improved performance-per-watt of Hopper actually makes it more efficient for the work done; you might use one H100 where previously two or three A100s were needed, ultimately saving energy for the same task.
• Form Factors: The A100 came in PCIe and SXM (GPU module) versions, and so does the H100. NVIDIA even offers a special dual-GPU H100 NVL (two H100 chips on one board with NVLink) targeted at large language model inference. In any case, whether you need a PCIe card or an HGX server module, H100 is available in similar form factors to A100, making upgrades straightforward.

In summary, the H100 is dramatically faster than the A100 across almost all metrics. For organizations pushing the limits of AI or HPC, upgrading to H100 means getting results in a fraction of the time. The A100 is still very capable, but H100 is the new gold standard if you need maximum throughput.

H100 for AI/ML and Large Language Models (LLMs)

The Nvidia H100 truly shines in AI workloads, especially for training and deploying large neural networks:

• Transformers and LLMs: The Hopper architecture is tailored for transformer-based models (which power today’s NLP and LLMs). The H100’s Transformer Engine can automatically alternate between FP8 and FP16 precision to boost speed while maintaining model accuracy. For large models like GPT-3 and beyond, this means significantly shorter training times. NVIDIA reported up to 4× faster GPT-3 training just by using H100 GPUs over A100 , and even higher gains (up to 9× in some cases) when leveraging the full Hopper optimizations in multi-GPU clusters. For AI researchers and companies working on cutting-edge LLMs or GPT-based services, the H100 can be transformative – what took days might now take hours.
• Deep Learning Training: Whether it’s computer vision, natural language processing, or recommendation systems, H100 provides a hefty boost in training throughput. More matrix math ops per second, more memory to fit bigger batch sizes or larger models, and faster data feeding from memory all contribute. This allows data scientists to experiment faster, train bigger models, and fine-tune with larger datasets without blowing out time budgets. It’s an AI accelerator in the truest sense – many see training speedups of 2–3× on typical models without any special tweaks, just by using H100 over the previous generation.
• AI Inference and Deployment: The H100 isn’t just for training – it’s also excellent for inference, especially for complex models that demand a lot of compute (e.g. running a 175B parameter GPT-3 to generate text). It supports INT8 and FP8 precisions which are often used for efficient inference. For instance, deploying a large language model on an H100 can yield much lower latency and higher throughput than on A100, meaning you can serve more queries per second or handle bigger inputs (like longer context in an LLM) before hitting hardware limits. If you’re building an AI application that needs to respond in real-time or serve many users, the H100 gives you extra headroom.

Importantly, all major AI frameworks (TensorFlow, PyTorch, JAX, etc.) have been updating to take advantage of Hopper’s features. Tools like NVIDIA’s CUDA and libraries (cuDNN, TensorRT) are optimized for H100, so developers can fairly easily unlock its performance potential.

H100 for Scientific Computing and HPC

Beyond machine learning, the H100 is also a beast for high-performance computing (HPC) workloads. Its improved double-precision and specialized instructions open new possibilities for researchers and engineers:

• High-Precision Calculations: With up to 3× the FP64 performance of A100, the H100 can tackle traditional HPC tasks (like linear algebra, simulations, CFD, climate modeling, etc.) much faster. For example, modeling physics phenomena or running large-scale simulations (common in scientific research and engineering) will see substantial speed-ups. This can accelerate time-to-discovery in research or time-to-market for engineering projects.
• Dynamic Programming & Algorithms: The new DPX instructions in Hopper target algorithms that use dynamic programming (a method used in many optimization and genomics problems). Nvidia demonstrated up to 7× faster execution for the Smith-Waterman algorithm (used in DNA sequence alignment) and other DP tasks versus A100. So bioinformatics and operations research applications can get a big boost from H100.
• Large-Scale HPC Systems: H100 GPUs are being deployed in supercomputers and HPC clusters worldwide. With the combination of huge memory, fast interconnects (NVLink and InfiniBand networking), and strong compute, they are ideal for multi-GPU distributed computing. In fact, an H100-based cluster with NVLink Switches can achieve phenomenal scaling efficiencies – enabling researchers to tackle exascale levels of computation for AI + HPC convergence workloads. If your work involves both simulation and AI (e.g. using deep learning alongside traditional modeling), H100 provides a unified platform to accelerate both types of workloads effectively.

Deploying Nvidia H100 in the Cloud

Despite its incredible capabilities, the H100’s high cost and power requirements mean not everyone can have one sitting under their desk. This is where cloud GPUs come in. Platforms like Runpod offer Nvidia H100 instances on-demand, so you can leverage this cutting-edge hardware without owning it outright. Here’s why using H100s via the cloud is a game-changer:

• Instant Access, No Infrastructure Hassle: With a cloud service, you can get access to H100 GPUs in minutes. There’s no need to purchase hardware (which can cost tens of thousands of dollars per card) or set up specialized power and cooling. On Runpod’s platform, for example, you can spin up a GPU pod with an H100 in a few clicks. It’s ready to go in a secure environment with the OS and frameworks of your choice. This means individuals and small teams can experiment with H100s that would otherwise be out of reach.
• Pay-As-You-Go Efficiency: Cloud providers typically charge by the minute or hour for GPU usage. The H100, being top-of-the-line, used to command a premium price – but costs have dropped dramatically as availability increased. Now you can rent an H100 for as low as a few dollars per hour. (On Runpod’s Pricing page, you’ll see H100 instances starting around ~$2/hour, which is extremely cost-effective given the compute you get.) If you only need H100 power for a short project or to burst-train a model occasionally, paying per use is far cheaper than investing in on-prem hardware that might sit idle.
• Scalability and Flexibility: Need more than one H100? With cloud, you can deploy multiple H100 GPUs on demand – whether for distributed training or to serve many inference endpoints. And when you don’t need them, you can shut them down and stop paying. This elasticity is perfect for startups and research labs: you scale your GPU usage to match your current needs. Runpod even offers serverless endpoints for AI inference, where your model can auto-scale across GPUs. For instance, you could use a serverless deployment to handle bursts of traffic, scaling from 0 to N serverless H100 endpoints seamlessly without managing the servers yourself (this uses Runpod’s Serverless Endpoints feature).
• Secure and Reliable Environment: Runpod provides H100s in its Secure Cloud environment – meaning the GPUs run in Tier 3/4 data centers with enterprise-grade security, compliance, and reliability. Your workload benefits from stable power, redundant networking, and strict isolation from other tenants. (There’s also a Community Cloud option which offers more cost savings by using community-provided hardware with a bit less redundancy, giving users flexibility in choosing price vs. absolute reliability.) In either case, your H100 instances in the cloud are production-ready and monitored 24/7.
• Bring Your Own Container: One of the advantages of modern cloud GPU platforms is the ability to deploy custom environments easily. With Runpod you can launch any Docker container on an H100 instance – so you have complete control over your software stack. Whether it’s a specific version of PyTorch, TensorFlow, or a custom CUDA extension, you just package it in a container and run it on the cloud GPU. This makes it simple to port your existing training scripts or inference services to the cloud with minimal changes. (Runpod’s platform supports both interactive notebooks and long-running jobs, as well as one-click templates for popular frameworks.)

Multiple Callouts for Runpod Sign-Up: If you’re ready to explore the Nvidia H100’s capabilities, there’s never been a more accessible way to do it. You can sign up for Runpod for free and launch an H100 instance in minutes. The intuitive dashboard and API let you manage jobs easily, and you’ll be riding on one of the world’s fastest GPUs. Whether you want to train a state-of-the-art AI model, fine-tune an LLM, or run heavy scientific computations, the H100 on Runpod has you covered. Don’t miss out on the opportunity to accelerate your projects – get started with a H100 on Runpod today!

(Interested in more tips and updates on AI infrastructure? Check out the Runpod Blog for the latest insights on GPUs, cloud, and machine learning trends.)

FAQs about the Nvidia H100

Q: What are Nvidia H100 GPUs used for?

A: The H100 is primarily used for AI/ML training and inference (especially large models and deep neural networks) and for high-performance computing tasks. It’s the go-to GPU for training large language models (like GPT-series), complex computer vision models, and other AI research that needs massive compute power. In HPC, it’s used for scientific simulations, data analytics, and any workload that benefits from fast matrix math or high-precision computation.

Q: How is the H100 different from the previous-generation A100?

A: The H100 is substantially more powerful. It has more than double the cores of the A100, 3× the standard FP32 compute, and adds new features like 4th-gen Tensor Cores with FP8 precision (allowing 4–9× faster AI performance in some cases) . It also has higher memory bandwidth (HBM3 vs HBM2e) and faster interconnects (NVLink 4, PCIe 5.0) for better multi-GPU scaling. Essentially, H100 can do the same work in a fraction of the time. The trade-off is a higher power draw (approx 700W vs 400W), but in return you get the absolute cutting-edge in GPU performance.

Q: Can I access an Nvidia H100 GPU in the cloud?

A: Yes – cloud providers like Runpod make H100 GPUs available on-demand. You can rent H100 instances by the hour on platforms such as Runpod’s Cloud GPUs service, which gives you immediate access to H100 without buying the hardware. This is ideal for developers, researchers, or businesses who need H100 performance for projects but want to avoid huge upfront costs. Simply launch an H100 cloud instance, run your workloads, and shut it down when done. You’ll only pay for what you use, and you get the benefits of a managed, secure infrastructure. It’s one of the fastest ways to start using H100 for your AI or computing tasks – just sign up on Runpod and launch an instance. Within minutes, you’ll be harnessing the same GPU technology that powers state-of-the-art AI breakthroughs.