NVIDIA HGX H100 4-GPU
baseboard
4× H100
NVLink
2022
FP64
0.1
PFLOPS
FP32
0.3
PFLOPS
Power
4.2
kW Total
Memory
320
GB Total
HGX H100 4-GPU configuration for smaller deployments
FP64
0.14
PFLOPS
32.4 TFLOPS/kW
FP32
0.27
PFLOPS
63.8 TFLOPS/kW
TF32
1.98
PFLOPS
941.9 TFLOPS/kW
FP16
3.96
PFLOPS
1884.8 TFLOPS/kW
System Details
GPU Configuration
System Specifications
Form Factor: baseboard
Total Power: 4.2 kW
Total Memory: 320 GB
Memory Bandwidth: 13400 GB/s
Precision Performance Breakdown
| Precision | System Performance | Per GPU | Efficiency |
|---|---|---|---|
| FP64 | 0.136 PFLOPS | 34.0 TFLOPS | 32.4 TFLOPS/kW |
| FP32 | 0.268 PFLOPS | 67.0 TFLOPS | 63.8 TFLOPS/kW |
| TF32 | 1.978 PFLOPS | 494.5 TFLOPS | 941.9 TFLOPS/kW |
| FP16 | 3.958 PFLOPS | 989.5 TFLOPS | 1884.8 TFLOPS/kW |
| BF16 | 3.958 PFLOPS | 989.5 TFLOPS | 1884.8 TFLOPS/kW |
| FP8 | 7.916 PFLOPS | 1979.0 TFLOPS | 3769.5 TFLOPS/kW |
| INT8 | 7.916 PFLOPS | 1979.0 TFLOPS | 3769.5 TFLOPS/kW |
Powered by NVIDIA H100
This system utilizes 4 × NVIDIA H100 SXM5 80GB GPUs, each delivering exceptional performance for AI and HPC workloads.
Per GPU TDP
700W
Per GPU Memory
80 GB
Process Node
4nm
Architecture
Hopper
Documentation & Resources
workstation-datasheet-dgx-spark-gtc25-spring-nvidia-us-3716899-web.pdf
NVIDIA • 2025-10-16
NVIDIA DGX Documentation
System guides, deployment resources
Typical Use Cases
Large Language Model Training
Distributed Deep Learning
Multi-GPU Inference
HPC Simulations
Scientific Computing
The NVIDIA HGX H100 4-GPU with 4× H100 GPUs is designed for enterprise-scale workloads requiring 0.1 PFLOPS of compute power with NVLink interconnect for efficient multi-GPU communication.