Compute Comparison
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Hopper

NVIDIA H100 PCIe 80GB

wins

4

The H100 PCIe is the more affordable form factor of the H100.

Budget Hopper inferencePCIe server deploymentsCost-sensitive LLM serving

Ampere

NVIDIA RTX 3090

wins

0

The RTX 3090 remains a popular budget option for AI workloads with 24GB GDDR6X.

Budget fine-tuningSmall model inferenceHobbyist AI

Performance comparison

Visual bars — winner highlighted

NVIDIA H100 PCIe 80GB

Metric

NVIDIA RTX 3090

FP16 TFLOPS

756 T
71 T

Bandwidth

2.0k GB/s
936 GB/s

VRAM

802 GB
246 GB

TDP (lower=better)

350 W
350 W

TFLOPS/watt

2.16
0.20

GB/s per watt

5.71
2.67

Full specification table

SpecNVIDIA H100 PCIe 80GBNVIDIA RTX 3090
ArchitectureHopperAmpere
VRAM80 GB HBM2e24 GB GDDR6X
VRAM typeHBM2eGDDR6X
Memory bandwidth2,000 GB/s936 GB/s
FP16 TFLOPS756 TFLOPS71 TFLOPS
TDP350 W350 W
TFLOPS/watt2.16 T/W0.20 T/W
GB/s per watt5.71 GB/s/W2.67 GB/s/W
NVLinkNoNo
Release year20222020

Live cloud pricing

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Power efficiency analysis

TFLOPS/watt and GB/s/watt — critical for data center TCO

NVIDIA H100 PCIe 80GB

FP16 TFLOPS756 TFLOPS
TDP350 W
TFLOPS/watt2.160 T/W
GB/s per watt5.71 GB/s/W

NVIDIA RTX 3090

FP16 TFLOPS71 TFLOPS
TDP350 W
TFLOPS/watt0.203 T/W
GB/s per watt2.67 GB/s/W

TFLOPS/watt measures compute efficiency — how much AI throughput you get per watt of power consumed. For data centers with PUE of 1.2–1.5, a 10% improvement in TFLOPS/watt translates directly to lower electricity costs and cooling requirements. GB/s/watt measures memory bandwidth efficiency, which is the binding constraint for memory-bound LLM inference workloads.

When to choose each GPU

Choose NVIDIA H100 PCIe 80GB for:

  • Budget Hopper inference
  • PCIe server deployments
  • Cost-sensitive LLM serving

Weaknesses / Limitations

  • Lower FP16 TFLOPS (756) and bandwidth (2,000 GB/s) than H100 SXM5
  • No NVLink — cannot scale across cards for very large models
  • Higher cost than A100 with only moderate performance advantage

Choose NVIDIA RTX 3090 for:

  • Budget fine-tuning
  • Small model inference
  • Hobbyist AI

Weaknesses / Limitations

  • Older Ampere architecture — significantly lower TFLOPS than Ada equivalents
  • 350W TDP — high power draw for a 24 GB consumer card
  • No ECC memory — not suitable for production reliability requirements