Breaking News: nVidia H100 Sets World Record Training GPT3 in Only 11 Minutes!

Table of Contents

1. Introduction
2. NVIDIA GPU Benchmark
   1. Comparison with A100 and V100 GPUs
   2. Power consumption and speed
3. AMD's Competition
   1. Performance comparison
   2. Industry standard and software
4. Moore's Law and Scaling
   1. Slowing down of Moore's Law
   2. Linear scaling achieved by NVIDIA
5. Cloud Provider and MLPerf Benchmark
   1. Cluster from CoreWeave
   2. MLPerf benchmark
6. NVIDIA's Superiority
   1. Generalized benchmarks
   2. NVIDIA's balance of technologies
7. Efficiency and Climate Angle
   1. NVIDIA GPUs for efficiency
   2. Climate impact
8. Conclusion

NVIDIA Breaks Record in AI Training with H100 GPUs

In recent news, NVIDIA has demonstrated yet again why they are regarded as the leader in GPUs for AI applications. They achieved a groundbreaking feat by combining 3,584 H100 GPUs using the cloud provider CoreWeave to train GPT-3, an early version of Chat GPT from OpenAI. The results were staggering, as the H100 GPUs managed to train the entire model in just 46 hours, compared to the 36 days required by the state-of-the-art A100 80GB and 51 days by the V100 GPUs. This remarkable accomplishment raises several interesting questions and highlights the dominance of NVIDIA in the AI space.

NVIDIA GPU Benchmark

Comparison with A100 and V100 GPUs

NVIDIA's achievement with the H100 GPUs is even more impressive when compared to their previous flagship models, the A100 80GB and V100 GPUs. Despite using the same number of physical GPUs, the H100s outperformed the A100s and V100s by a significant margin. The H100s' ability to train GPT-3 20 times faster than the previous state-of-the-art GPUs showcases the immense progress made by NVIDIA.

Power consumption and speed

One of the astonishing aspects of NVIDIA's breakthrough is that the H100 GPUs consumed less power than their predecessors while achieving significantly faster training times. This highlights the advancements made not only in the GPUs themselves but also in the platform, networking, and software developed by NVIDIA. Despite AMD's introduction of new accelerators like the Mi 2500 and 3000, NVIDIA's industry-standard software and the extensive usage by top developers give them a clear AdVantage.

AMD's Competition

Performance comparison

While the speed at which the H100 GPUs trained GPT-3 is remarkable, it begs the question of whether AMD can compete with NVIDIA. Although AMD's accelerators Show promise, the industry has predominantly adopted NVIDIA's GPUs due to their superior software and developer support. Furthermore, the benchmarking conducted by NVIDIA has not been replicated on AMD GPUs, casting doubt on their ability to match NVIDIA's performance.

Industry standard and software

AMD's attempt to establish itself as a serious competitor to NVIDIA inadvertently fortifies NVIDIA's dominant position in the AI space. The industry recognizes NVIDIA as the standard owing to their unmatched software and technological advancements. Choosing AMD's GPUs entails a certain level of risk and uncertainty, even if their performance is on par with NVIDIA's.

Moore's Law and Scaling

The Notion of Moore's Law, which describes the rapid increase in computing power over time, has shown signs of slowing down. However, what truly matters is not the density or energy consumption of compute power but its scalability. NVIDIA has demonstrated near linear scaling with their highest-end GPUs, especially with the new InfiniBand interface that seamlessly connects multiple GPUs as one. This breakthrough allows for exponential performance gains as more GPUs are added, making scalability an essential aspect of AI training.

Cloud Provider and MLPerf Benchmark

NVIDIA's achievement was made possible thanks to the collaboration with CoreWeave, a cloud provider specializing in GPU clusters. This dynamic cluster allocation, along with the independent MLPerf benchmark, played a vital role in validating NVIDIA's superiority. MLPerf annually compiles a range of standardized benchmarks, including recurrent neural networks, GANs, LLMs, and Generative AI. NVIDIA's GPUs scored the highest in each category, further solidifying their position as the industry leader.

NVIDIA's Superiority

NVIDIA's success in training GPT-3 with the H100 GPUs showcases their unparalleled superiority in the AI field. Their GPUs, which are essentially application-specific integrated circuits (ASICs), strike a balance between being specialized and versatile. This unique combination enables NVIDIA to deliver exceptional performance across all categories of the MLPerf benchmark.

Efficiency and Climate Angle

In addition to their unmatched performance, NVIDIA GPUs also offer excellent efficiency. The density achieved by clustering NVIDIA GPUs makes them a desirable option for those looking to optimize power consumption. With the increasing demand for AI computations, this efficiency becomes even more crucial in reducing the carbon footprint of AI infrastructure.

Conclusion

NVIDIA's accomplishment of training GPT-3 in just 46 hours using 3,584 H100 GPUs exemplifies their dominance in the AI GPU market. Their GPUs outperform the competition not only in terms of speed but also in efficiency and scalability. With their industry-standard software, robust ecosystem, and continuous technological advancements, NVIDIA remains the clear choice for AI researchers and developers.