As AI models become more sophisticated and resource-intensive, the need for high-performance GPUs (Graphics Processing Units) has been felt acutely. Enter the NVIDIA H100 —a GPU that represents a quantum leap in the realm of AI computing.

 

Long recognized as the pioneer in GPU technology, NVIDIA has again raised the bar with the NVIDIA H100 . Built on the cutting-edge Hopper architecture, the NVIDIA H100 is redefining what’s possible in AI computation.

 

We’ll dive deep into the capabilities of the H100 while also exploring the best use cases for the NVIDIA H100 and its limitations.

 

 

NVIDIA H100 : An Overview

 

The NVIDIA H100 is a high-performance computing solution that features significant improvements when compared to its predecessors.

 

One of the defining features of the H100 is its impressive number of cores. It has 640 Tensor Cores, 14592 CUDA cores, and 128 Ray Tracing Cores all of which facilitate high-speed data processing. Experts have been able to achieve up to 9 times faster training of AI models and 30 times faster inference with the H100 than the A100. Given that the A100 was released just two years back, the H100 is clearly more than just an incremental improvement.

 

Another feature that deserves to be mentioned is the NVIDIA H100 ’s Transformer Engine. An essential component of Tensor Cores, the transformer engine is specifically designed to train large language models (LLMs).

 

The NVIDIA H100 also boasts the fourth-generation NVLink that helps connect multiple GPUs to form an integrated system. This leads to efficient data transfers between GPUs and boosts performance.

 

Features that Make NVIDIA H100 Stand Out

 

Here are a few features of the NVIDIA H100 that lend it powerful computing capabilities:

 

Fourth-Generation Tensor Cores – Increased Performance Efficiency

 

At the heart of the NVIDIA H100 are its fourth-generation Tensor Cores. These cores deliver a remarkable 60x improvement in performance efficiency compared to the previous Ampere architecture.

 

A key innovation powering this leap is the integrated Transformer Engine. This specialized component in each Tensor Core enables a game-changing capability: the dynamic switching between FP8 and FP16 data formats.

 

Typically, FP8 computations offer double the processing power of FP16, reducing operational costs. However, this efficiency comes at the cost of precision. The Transformer Engine’s dynamic format switching solves this tradeoff. It intelligently alternates between FP8 and FP16 as needed. This allows users to maximize efficiency while maintaining the accuracy required for AI workloads.

 

Superior FP32 Performance – Enhanced Computational Power

 

FP32, or single-precision floating-point, is a common data format used in scientific computing, graphics rendering, and certain AI workloads. The measurement for FP32 performance is TFLOPS, which represents the number of floating-point operations the GPU can complete per second.

 

The NVIDIA H100 delivers an impressive 67 TFLOPS of FP32 performance. This is a notable increment from the 19.5 TFLOPS of the previous-generation A100. Please refer to the table below for a side-by-side comparison of NVIDIA H100 and A100. The increase in computational power makes the NVIDIA H100 fit for calculations that won’t be affected by a bit of approximation—think complex simulations or climate models.

 

DPX Instructions – Accelerated Dynamic Programming

 

Dynamic programming is a problem-solving technique that breaks down complex problems into simpler sub-problems, storing and reusing the solutions to improve computational efficiency. The NVIDIA H100 speeds up dynamic programming with a unique feature—DPX instructions. Acceleration in dynamic programming makes the NVIDIA H100 useful in areas like genomics, cryptography, and image processing.

 

Multi-Instance GPU – Better Resource Utilization

 

The NVIDIA H100 comes with second-generation multi-instance GPU technology. The latter enables a single GPU to be partitioned into seven isolated instances. Each instance has dedicated compute units, memory, and cache.

 

Such a feature allows the flexibility to provision instances depending on the size and complexity of a workload. It results in optimal utilization of the available resources. It also ensures better returns on data center investments.

 

Fourth-Generation NVLink – Accelerated Data Transfer

 

NVLink is bidirectional interconnect hardware that allows GPUs to share data. The NVIDIA H100 ’s fourth-generation NVLink technology delivers 900 GB/s of bidirectional bandwidth between GPUs. This enables seamless data exchange and collaboration in multi-GPU systems.

 

The H100 also supports PCIe Gen5. The latter offers 128 GB/s of bidirectional bandwidth between the GPU and the CPU. This further speeds up data transfer, improving performance in demanding AI applications.

 

Enhanced Memory Bandwidth – Faster Processing

 

Memory bandwidth represents a GPU’s ability to transfer data to and from its memory, measured in bytes per second. This crucial metric impacts performance, particularly in data-heavy workloads.

 

The NVIDIA H100 offers 3 TB/s of bandwidth, nearly double that of NVIDIA A100’s 1.6 TB/s capability. This ensures faster reading and writing of data to the memory, reducing processing delays. Improved bandwidth proves useful for workloads involving large datasets, e.g. AI model training, video processing, and scientific computations.

 

TDP Balancing Performance – Optimal Power Use

 

TDP or Thermal Design Power indicates the maximum amount of heat a GPU can generate while operating under normal conditions. For NVIDIA H100 , the TDP ranges anywhere between 300-700 W depending on the model.

 

Models with higher TDP deliver better performance, but they need more powerful cooling systems. Lower TDP models, in contrast, offer a balance of performance and power usage, fitting well in environments with limited cooling capabilities.

 

Additionally, NVIDIA’s Hopper architecture brings notable improvements in power efficiency. This enhanced efficiency is valuable for data centers looking to balance computational power with energy costs.

 

NVIDIA H100 vs NVIDIA A100: NVIDIA H100 Surpasses A100 in Performance

 

The NVIDIA H100 represents a transformative change over all its predecessors, including the A100, across a range of capabilities.

 

In tests conducted by NVIDIA, the NVIDIA H100 has been found to deliver up to 30 times better inference performance compared to the A100. Plus, when training large language models in a multi-GPU cluster setup, the NVIDIA H100 demonstrated remarkable speed improvements—up to 9 times faster than the A100 cluster.

 

Let’s have a look at some of the key technical specifications between the two GPU architectures:

 

Feature	A100	H100
Architecture	Ampere	Hopper
GPU Memory	40 or 80 GB HBM2e	80 MB HBM3
GPU Memory Bandwidth	1.6 TB/s	3 TB/s
FP32 performance (TFLOPS)	19.6	67
FP64 performance (TFLOPS)	9.7	33.5
CUDA Cores	6912	14592
Max Thermal Design Power	Up to 400 W	300-700 W
TF32 Tensor Core Flops	312	989
FP16 Tensor Core TFLOPS	624	1979
FP8 Tensor Core TFLOPS	NA	3958
Target market	AI, data analytics, HPC	AI, graphics, HPC
Cost*	$10,000-$15,000	$25,000-$30,000

 

*Cost will vary depending on the model chosen and the vendor.

 

NVIDIA H100 Tensor Core GPU 80GB PCIe

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When to Use NVIDIA H100 : Best Fit Use Cases

 

The NVIDIA H100 is a highly versatile GPU. Some of its use cases include:

 

• High-Performance Computing: The NVIDIA H100 is well-suited for applications that require fast, high-precision calculations. These include financial modeling, scientific simulations, and weather predictions.
• Deep Learning: The NVIDIA H100 ‘s powerful Tensor Cores make it an exceptional choice for training complex neural networks. Its processing capabilities make it ideal for areas like natural language processing and computer vision.
• AI Inference: Owing to exceptional memory bandwidth, NVIDIA H100 excels at real-time inference tasks, such as image recognition and speech processing.
• Computational Biology: The NVIDIA H100 ‘s DPX instruction set makes it a valuable tool for complex tasks including genome sequencing, protein folding simulations, and disease diagnosis.

 

 

Limitations of NVIDIA H100

 

While the NVIDIA H100 offers unparalleled performance, it comes with certain limitations in terms of memory and power consumption.

 

The NVIDIA H100 boasts a substantial 80GB of HBM2e memory, along with a memory bandwidth of 3 TB/s. But how much of this memory will be available for use will depend on the server configuration as well as the use case.

 

Similarly, the NVIDIA H100 ‘s power consumption, with a maximum of 700W, is dynamically managed to maintain efficiency. Yet, power availability can still limit processing for certain workloads.

 

Another important factor is pricing. NVIDIA H100 is one of the most expensive GPUs out there. It is almost 70-80% more expensive than the A100. While it is possible to reduce the total cost of ownership for NVIDIA H100 by optimizing the workloads, it makes sense to opt for such expensive hardware only if you plan to build large language models or complex neural networks.

 

Conclusion

 

The NVIDIA H100 has indeed transformed the landscape of AI and high-performance computing. Its robust features make it the preferred choice for running large language models, scientific simulations, and deep learning solutions.

 

If you’re interested in harnessing the power of the NVIDIA H100 , our team of experts is here to help. Uvation’s team has deep expertise in building tech stacks that are a custom fit for different businesses. Our clients not only benefit financially but also accrue synergies for their technological investments. Our experience will help you get the maximum value and performance out of your AI investments.