In this age of high-performance computing, when researchers are working on sophisticated AI and LLM solutions, Graphics Processing Units (GPUs) have emerged as an enabler. Designed originally for video games and graphics, these GPUs excel at data-intensive and compute-intensive tasks.

 

NVIDIA, a leading technology company, has built some of the most powerful GPUs in the world. Two of their flagship GPU models—A100 and H100—have become indispensable to the modern computing world. The debate around NVIDIA H100 vs A100 is particularly relevant for those exploring cutting-edge technologies. NVIDIA A100, released in 2020, is a game-changer when it comes to handling AI workloads and complex calculations. Not surprisingly, it is the preferred choice for many research labs and data centers.

 

The newer H100, released in 2022, has raised the bar even more. The GPU can tackle the most challenging computing tasks, from training large language models (LLMs) to processing neural networks.

 

No wonder, both these GPUs represent a major step forward in high-performance computing. However, when it comes to choosing one for their project, users face the dilemma of which GPU they should go for.

 

In this blog, we have analyzed the two GPUs to help you decide which one is best suited for your project goals and budget.

 

 

A100 vs H100: An Overview

 

The NVIDIA H100 vs A100 comparison begins with architecture. The NVIDIA A100 GPU, built on the Ampere architecture, comes with several key improvements over its predecessors. One of its standout features is its powerful 3rd generation Tensor Cores. These processors provide a significant boost in performance, making it suitable for areas such as AI, scientific computing, and data analytics.

 

Another feature that was first introduced in the A100 is its Multi-Instance GPU (MIG) capability. This allows the GPU to be partitioned into smaller, independent instances. As a result, it can run multiple applications concurrently. This ensures efficient use of resources.

 

An upgrade over the A100 is NVIDIA’s H100. Built on the Hopper architecture, the H100 is designed with a strong focus on improving AI computations. It features 4th generation Tensor Cores optimized for training large language models.

 

The H100 also comes with an innovative Transformer Engine, a feature that speeds up the training of transformer models. For those who don’t know, transformer models are neural networks that power natural-language processors such as GPT-3. Its memory bandwidth is also much higher than the A100. The H100 also supports fourth-generation NVLink technology that enables seamless data transfer in multi-GPU environments.

 

Now let’s see how the A100 and H100 stack up against each other in terms of computing power, memory, storage, energy efficiency, cost, and other crucial parameters.

 

Processing Power

 

The processing capabilities of NVIDIA H100 vs A100 GPUs reveal substantial differences. Every A100 GPU has 432 Tensor Cores, 6912 CUDA Cores, and 108 Streaming Multiprocessors (SMs). In contrast, each H100 GPU has 456 Tensor Cores, 14,592 CUDA Cores, and 114 Streaming Multiprocessors (SMs). The increased number of cores in NVIDIA H100 translates to more robust computing capabilities.

 

The A100 delivers exceptional performance across many benchmarks. Equipped with third-generation Tensor Cores, the A100 offers up to 9.7 TFLOPS for double-precision (FP64) operations and 19.5 TFLOPS for single-precision (FP32) operations. This makes the A100 a top contender for computing tasks that demand high precision.

 

The H100 comes with fourth-generation Tensor Cores that provide even higher TFLOPS for both formats—34 for double-precision and 60 for single-precision operations. It also supports the FP8 format which the A100 doesn’t. As a result, it enables even faster training and inference without sacrificing accuracy in AI and deep learning models.

 

Additionally, the H100 has many exclusive features that take it several notches up its predecessor. These include:

 

• DPX Instructions: DPX instructions speed up dynamic programming. Dynamic programming is a problem-solving technique where complex problems are broken down into simpler sub-problems to reduce computational complexity. This feature makes the H100 appropriate for verticals like healthcare and robotics.
• Thread Block Cluster: Another feature introduced in the H100 is thread block clusters. Multiple thread blocks running on different streaming microprocessors (SMs) can be grouped into a cluster. Users can programmatically control these clusters, improving efficiency in computational tasks.
• Asynchronous Execution: The H100 also features a Tensor Memory Accelerator (TMA) that enables more efficient data transfers between the memory and SM.

 

Memory

Memory bandwidth and capacity are critical in the NVIDIA H100 vs A100 comparison. The A100’s 40-80 GB of HBMe memory provides a bandwidth of 1.6 TB/s. This enables fast processing of massive datasets and complex AI models. The H100, in contrast, comes with HBM3 memory with almost double the bandwidth (3.3 TB/s) of the A100. It also features an L2 cache of 50 MB. These specifications ensure faster data retrieval and processing in data-heavy workloads.

 

Connectivity

 

When evaluating connectivity in NVIDIA H100 vs A100 , the A100 supports third-generation NVLink and NVSwitch with a bandwidth of 600 GB/s that allow seamless data exchange between GPUs. NVLink proves useful in large-scale deployments where multiple GPUs work together.

 

The H100’s fourth-generation NVLink and NVSwitch with 900 GB/s bandwidth have better GPU clustering capabilities. They ensure faster data transfer and improved communication between GPUs.

 

Power Efficiency

 

Energy consumption is another vital aspect of the NVIDIA H100 vs A100 debate. Both the A100 and H100 have been designed with efficiency in mind. The A100, operating at a TDP of 300 W, is overall more energy-efficient than the H100. On the other hand, the H100 with a TDP of 400-700 W consumes much more power and, therefore, requires more robust cooling systems.

 

Suitability for Different Use Cases

 

The NVIDIA H100 vs A100 comparison often boils down to use cases. As discussed above, NVIDIA H100 has been optimized for training large language models. That’s why it is a great fit for applications related to generative AI, sentiment analysis, or language translation. Also, H100’s unmatched parallel processing capabilities make it suitable for use in scientific simulations, climate modeling, and image processing.

 

As far as the A100 is concerned, it is highly versatile and suitable for less demanding workloads—AI inference and training, advanced analytics, financial modeling, and fraud detection. It provides adequate resources needed to efficiently run such workloads without requiring high investment.

 

Cost

 

Cost considerations significantly impact the NVIDIA H100 vs A100 decision. Owing to its far more powerful computing capabilities, the H100 costs far more than the A100. The cost, however, varies depending on whether you want to run workloads in the cloud or buy GPU hardware.

 

For the cloud, you may have to shell out anywhere between $1.2-$1.8 per hour for the A100 and $2-$3 per hour for the H100. That said, the H100 takes less time to train models. Lower training time means the GPU will be used for fewer hours. This brings down the overall cost for the H100.

 

Alternatively, if you buy the hardware, you may need to spend around $10,000-$15,000 for the A100 and $25,000-$30,000 for the H100. In addition to these upfront expenses, you need to factor in the cost of running the servers, including power consumption and cooling systems.

 

It’s important to note that while the H100 can be optimized for efficiency, it makes sense to invest in the H100 only if you intend to work with LLMs, scientific simulations, or neural networks. For smaller projects with simpler workloads, the A100 provides better value for money.

 

 

Longevity and Value

 

Another crucial factor in the NVIDIA H100 vs A100 discussion is hardware longevity. The H100, with its advanced architecture and features, is more future-proof than the A100. This ensures better resale value and longer support from NVIDIA.

NVIDIA A100 vs H100: A Comparison of the Specifications

The H100 with an increased number of Tensor Cores, CUDA Cores, and SMs clearly has superior computational capabilities. Here is a table summarizing the key differences between the GPUs:

 

Feature	A100	H100
Architecture	Ampere	Hopper
GPU Memory	40 or 80 GB HBM2e	80 MB HBM3
GPU Memory Bandwidth	1.6 TB/s	3.3 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, LLMs, Deep Learning, Scientific Simulations
Cost*	$10,000-$15,000	$25,000-$30,000

*Cost will depend on the model chosen, form factor, and the vendor. 

 

H100 vs A100: The Final Verdict

 

So, we see the choice between A100 and H100 depends primarily on your specific needs and budget. However, in addition to price and performance, there is another crucial factor that should not be neglected when investing in a top-tier GPU—longevity. We all know that the H100 is better equipped to meet the computational needs of the present-day users than the A100. As a result, it is likely to last much longer before it reaches the end-of-life stage where NVIDIA stops manufacturing it or providing support for it. Also, the H100 will have a better resale value regardless of when it is sold. Given how NVIDIA has continually invested in technological enhancements, the overall lifespan (and value delivered over this period) of the hardware has to be taken into account too.

 

Choose the Right GPU for Your Workload!”

 

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