NVIDIAVendor documented
H100 SXM5
Hopper · Hopper · SXM5 · 2022
The workhorse training/inference GPU of the generation. Transformer Engine and FP8 make it the baseline for large-model work.
LLM trainingLLM inferenceFine-tuningHPCMultimodal
Precision fingerprint
6432t3216BF168i8
Memory
80 GB
HBM3
Bandwidth
3.35 TB/s
peak
TDP
700 W
air or liquid
Max model
~30B
FP16, planning est.
Compute throughput
| FP64 | 67 TFLOPS |
| FP32 | 67 TFLOPS |
| TF32 | 494 TFLOPS |
| FP16 | 989 TFLOPS |
| BF16 | 989 TFLOPS |
| FP8 | 1.98 PFLOPS |
| INT8 | 1.98 PFLOPS |
Platform & software
InterconnectNVLink 4 — 900 GB/s
PCIePCIe 5.0 x16
Coolingair or liquid
MIGSupported
PartitioningUp to 7× MIG
VirtualizationvGPU, MIG
FrameworksCUDA, TensorRT-LLM, Triton, NeMo, vLLM
AvailabilityAWS, Azure, GCP, OCI, bare-metal
Known limitations
- ·700 W demands rack-level power and thermal planning
- ·HBM3 supply-constrained through much of its life