10 Things You Need To Know About The NVIDIA H100

Few hardware components have had as transformative an impact on Artificial Intelligence development and performance as the NVIDIA H100. As the pursuit of automated solutions intensifies across industries, more companies are using the NVIDIA H100 Tensor Core GPU for AI training and inference.?

Whether you're an AI developer or simply curious about cutting-edge technology, here are ten things that you need to know about the H100:

1. Unprecedented AI Performance: The H100 boasts an astounding 4000 TFLOPS (teraflops) of AI performance, making it one of NVIDIA's fastest AI accelerators ever.

According to NVIDIA CEO Jensen Huang at the NVIDIA GTC 2024, training the GPT-MoE-1.8T model using 25,000 Ampere-based GPUs (most likely the A100) took 3 to 5 months. Doing the same with Hopper (H100) would take about 8,000 GPUs in 90 days.

2. Transformer Engine Innovation: Transformers have become the backbone of modern AI models. The H100's fourth-generation Transformer Engine is optimized for transformer-based workloads, significantly accelerating training and inference for tasks like language translation, text generation, and image recognition.

For instance, NVIDIA's H100 was used to train the MoE 395B model, which typically required seven days to train using the previous A100 GPUs. However, with the H100's advanced architecture and Transformer Engine, the training time was reduced to just 20 hours using 8,000 H100 GPUs.

3. 80 Billion Transistors: Based on NVIDIA's Hopper architecture, the H100 features an 80 billion transistor count, enabling unparalleled performance and efficiency. The Hopper architecture's design maximizes compute density and memory bandwidth, critical factors for large-scale AI workloads.

4. HBM3 Memory: The NVIDIA H100 is the first GPU to incorporate HBM3 memory technology. With a massive 80GB of HBM3 memory and a memory bandwidth of over 3TB/s, the H100 can seamlessly handle large datasets and complex AI models.

5. Programmable GPU: The H100 provides increased programmability, with features such as support for multiple precision formats (FP64, TF32, FP32, FP16, INT8, and FP8) and enhanced AI inference and trainingcapabilities.

Its design allows for efficient execution of a wide range of neural network architectures, making it suitable for diverse AI applications.

6. Multi-Instance GPU (MIG) Technology: The H100's MIG technology allows you to partition the GPU into multiple instances, each acting as a separate GPU. This benefits cloud environments and shared infrastructure, enabling multiple users or workloads to utilize the H100 simultaneously.

7. Applications Across Industries: The H100's impact extends far beyond AI research labs. It's finding applications in diverse industries, including healthcare (drug discovery, medical imaging), finance (fraud detection, risk analysis), manufacturing (predictive maintenance, quality control), and autonomous vehicles (perception, decision-making).

8. Accessibility Through Cloud Providers: If you don't have the resources to invest in an H100 infrastructure, some cloud providers like CUDO Compute offer H100 instances on their platforms, which democratizes access to powerful AI acceleration, allowing businesses and researchers to experiment with the H100 without significant upfront costs.

9. Software Ecosystem: NVIDIA's comprehensive software ecosystem, including libraries like cuDNN and frameworks like TensorFlow and PyTorch, is fully compatible with the H100. This means developers can use existing tools and workflows with the H100 without changes to their existing codebase.

10. Successful Implementations: Companies like Meta and Altair have used the H100 to drive their AI initiatives. Meta uses it for large-scale language models and Altair for cutting-edge simulations. These examples underscore the H100's real-world impact and its potential to transform entire industries.

The NVIDIA H100 empowers researchers, developers, and businesses to tackle complex challenges and drive transformative advancements across multiple industries. You can start using the NVIDIA H100 today on CUDO Compute on demand from as low as $3.49/hour.?

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