The industry workhorse for training large language models. H100 clusters power the majority of frontier AI labs, with the Transformer Engine automatically managing mixed-precision training for optimal throughput on models from 7B to 175B+ parameters.
Deploy production inference endpoints with native FP8 quantization. The H100's Transformer Engine delivers up to 30x inference speedup over A100 for large language models while maintaining model accuracy.
Accelerate molecular dynamics simulations, protein structure prediction, and genomic analysis. The H100's 3.35 TB/s memory bandwidth handles the massive datasets required for computational biology research.
Train and deploy vision transformers, video understanding models, and autonomous driving perception stacks. The H100 is the standard platform for production computer vision workloads across cloud providers.
| GPU Architecture | NVIDIA Hopper |
| Transistor Count | 80 Billion (4N Process) |
| CUDA Cores | 16,896 |
| Tensor Cores | 4th Gen (528 cores) |
| Memory Capacity | 80 GB HBM3 |
| Memory Interface | 5120-bit |
| Memory Bandwidth | 3.35 TB/s |
| L2 Cache | 50 MB |
| NVLink Bandwidth | 900 GB/s |
| Form Factor | SXM5 / PCIe |
| Thermal Design Power | 700W (SXM) / 350W (PCIe) |