NVIDIA HGX A100 4-GPU

baseboard
4× A100
NVLink
2020
FP64
0.0
PFLOPS
FP32
0.1
PFLOPS
Power
3.2
kW Total
Memory
320
GB Total

HGX A100 4-GPU configuration

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FP64
0.04
PFLOPS
12.1 TFLOPS/kW
FP32
0.08
PFLOPS
24.4 TFLOPS/kW
TF32
0.62
PFLOPS
195.0 TFLOPS/kW
FP16
1.25
PFLOPS
390.0 TFLOPS/kW

System Details

GPU Configuration

GPU Count: 4 GPUs
Architecture: Ampere
Interconnect: NVLink

System Specifications

Form Factor: baseboard
Total Power: 3.2 kW
Total Memory: 320 GB
Memory Bandwidth: 8.2 TB/s

Precision Performance Breakdown

PrecisionSystem PerformancePer GPUEfficiency
0.039 PFLOPS 9.7 TFLOPS 12.1 TFLOPS/kW
0.078 PFLOPS 19.5 TFLOPS 24.4 TFLOPS/kW
0.624 PFLOPS 156.0 TFLOPS 195.0 TFLOPS/kW
1.248 PFLOPS 312.0 TFLOPS 390.0 TFLOPS/kW
1.248 PFLOPS 312.0 TFLOPS 390.0 TFLOPS/kW
2.496 PFLOPS 624.0 TFLOPS 780.0 TFLOPS/kW

Where a vendor states a basis, the figure shown is the dense one, and any figure it publishes is listed separately. Vendors commonly headline the sparse number instead: for NVIDIA's 2:4 structured sparsity that is exactly twice the dense figure, though other vendors' sparse modes do not all follow that ratio.

Powered by NVIDIA A100

This system utilizes 4 × NVIDIA A100 SXM4 80GB GPUs, each delivering exceptional performance for AI and HPC workloads.

Per GPU TDP

400W

Per GPU Memory

80 GB

Process Node

7nm

Architecture

Ampere

Documentation & Resources

Flopper Spec Sheet

NVIDIA HGX A100 4-GPU specifications, generated from our database. Printable.

Vendor product page

NVIDIA HGX A100 4-GPU documentation

View ↗

workstation-datasheet-dgx-spark-gtc25-spring-nvidia-us-3716899-web.pdf

NVIDIA • 2025-10-16

View Document ↗

NVIDIA DGX Documentation

System guides, deployment resources

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Typical Use Cases

Large Language Model Training
Distributed Deep Learning
Multi-GPU Inference
HPC Simulations
Scientific Computing

The NVIDIA HGX A100 4-GPU runs 4× A100 GPUs over NVLink, delivering 1.25 PFLOPS BF16 dense.