Performance Comparison: NVIDIA A100, H100 & H800
Here's a comparison of the performance between Nvidia A100, H100, and H800:
Here's a comparison of the performance between Nvidia A100, H100, and H800:
Nvidia A100:
Released in 2020
Considered the previous generation flagship GPU for AI and HPC workloads
Offers 80GB HBM2e memory
Boasts strong performance in various applications, including machine learning, scientific computing, and data analytics
Nvidia H100:
Released in 2022 as the successor to A100
Offers significant performance improvements over A100
Features:
80GB HBM3 memory (higher bandwidth compared to A100)
New "Transformer Engine" for efficient handling of large language models
Improved architecture for faster processing
According to Nvidia, H100 delivers:
Up to 9x faster AI training performance
Up to 30x faster inference performance
Up to 3x faster performance in specific workloads compared to A100 with NVLink Switch System
Nvidia H800:
A modified version of H100 specifically sold in the Chinese market due to export regulations
Key difference:
Reduced chip-to-chip data transfer rate (around 300 GBps compared to H100's 600 GBps)
This reduction likely leads to:
Lower overall performance compared to H100
The exact performance impact is not officially confirmed by Nvidia
Additional points to consider:
The specific performance difference between A100 and H100/H800 can vary depending on the workload and application.
Cost is another crucial factor. H100 is generally more expensive than A100, and H800 might be priced differently due to its limited market availability.
Choosing the best GPU depends on your specific needs and budget. If you require maximum performance for demanding AI or HPC tasks, H100 might be the ideal choice. However, if cost is a major concern and your workloads are not as intensive, A100 could still be a viable option.
It's important to carefully evaluate your requirements and research specific benchmarks for your intended applications before making a decision.