Face-off between AI Powerhouses — A100 vs. H100! A Complete Analysis
The decade of AI, ruled by the chip making companies and compute accelerators. The decade which will be remembered for multiple decades. For many reasons, one of kind is Nvidia’s story from making Gaming chips to later targeting a total different space ‘Artificial Intelligence’.
In 2024, till today Nvidia did 80% of the revenue entirely from selling AI grade GPUs doubling the Total Sales Revenue of 26B$ in 2023 to 60B$ in first 5 months. It’s certainly affecting their stock price to sky rocket.
A big reason for this growth is that there was a big need for good computer chips to train, run and understand AI well. Nvidia’s Ampere chips did really well, and then the Hopper chips came and people liked them too. The H100 chip especially, is known for being super fast at training AI models. It’s faster than other chips that cost about the same. Nowadays, many AI companies choose the H100 until they can get the Blackwell series chips.
In what way have these two series had the most significant impact on the AI industry? We’ll find out the reality today in this article 📰
Let’s Compare A100 and H100 from Alternative Diagonals
By listening to Nvidia’s own benchmarks and efficiency tests, we find that the H100 provides twice the computing speed of the A100.
- It requires half the time to train and inference a Model. This saves many productive hours
- The H100 costs about twice as much as the A100. However, if the H100 completes tasks in half the time, the overall expenditure through a cloud model could be similar. This is because the higher price of the H100 is balanced by its faster processing time.
Let’s start comparing the configurations from below picture.
A100 was released in 2020, it was the first GPU to feature the Ampere architecture. Before H100 release, A100 used to be the first choice for Model makers due to it’s compatibility with AI. A substantial jump in Tensor Cores performance, increased CUDA core count for parallel processing, larger memory, and the fastest-ever memory bandwidth at 2 Tbps. In addition, it was the only edition that offered support for Multi-Instance GPU (MIG).
The world was convinced, that the only option to train AI models at that time was the A100. A100 GPU’s was a great option for training complex neural networks in deep learning and AI tasks. This is because it has powerful Tensor Cores and can handle a lot of computations quickly. Not only that, it was also successful in inferences related tasks such as image recognition, data analytics, language recognition, etc.
No one used to believe there can be better GPUs for AI related tasks, until 2022. Nvidia released the H100, automatically it became a #1 choice for everyone now.
FOR A REASON:
The Nvidia H100 GPU is designed to handle the most demanding AI tasks and large-scale data processing. It comes with advanced Tensor Cores that greatly improve AI training and inference speeds. It supports various types of computations, including double precision (FP64), single precision (FP32), half precision (FP16), and integer (INT8) tasks.
Compared to the A100, the H100 offers significant performance improvements:
- It’s six times faster, capable of reaching four petaflops for FP8 tasks.
- There’s a 50% increase in memory, using HBM3 high-bandwidth memory with speeds of up to 3 Tbps, and nearly 5 Tbps with external connectivity.
- It can train model transformers up to six times faster thanks to its new Transformer Engine.
What’s the core factor to Choose H100 over A100?
The H100 introduces a revolutionary chip design and several new features that differentiate it from its predecessor, the A100. Let’s delve into these updates to determine if your specific needs warrant consideration of the new model.
Enhanced Privacy with Confidential Computing One notable addition to the H100 is the introduction of Confidential Computing (CC). While data encryption at rest and in transit are common security measures, CC extends this protection to data while it’s in use. This feature could be particularly appealing for industries handling sensitive information, such as healthcare and finance, where maintaining privacy and compliance is paramount.
Optimized Performance with Tensor Memory Accelerator The Tensor Memory Accelerator (TMA) is a groundbreaking addition to the H100’s architecture. It offloads memory management tasks from GPU threads, leading to a significant boost in performance. Unlike simply increasing the number of cores, the TMA represents a fundamental architectural shift.
As the demand for training data grows, the TMA’s ability to seamlessly handle large datasets without burdening computation threads becomes increasingly valuable. Moreover, as training software evolves to fully leverage this feature, the H100 may emerge as the preferred choice for large-scale AI model training, offering enhanced futureproofing capabilities.
In summary, the H100’s advancements in both privacy and performance position it as a compelling option for organizations seeking cutting-edge solutions for their AI and data processing needs.
H100 was additionally specialized to have higher performance for transformer models. Whilst, the A100 offers more versatility, handling a broader range of tasks like data analytics effectively.
Choice will most likely depend upon the task, a certain AI model can take half of the time to train a large AI model but on the other hand the effective cost would be much larger than training on A100 grids.
