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Hopper

NVIDIA H100 SXM5 80GB

wins

4

The H100 SXM5 is the high-performance form factor of the H100.

LLM trainingMulti-GPU NVLink clustersHigh-throughput inference

Ada Lovelace

NVIDIA RTX 4060 Ti 16GB

wins

1

The RTX 4060 Ti 16GB doubles the VRAM of the RTX 4060 at 16GB GDDR6, enabling 7B–13B model inference at INT4/INT8.

Budget inferenceSmall model fine-tuningEntry Ada AI

Performance comparison

Visual bars — winner highlighted

NVIDIA H100 SXM5 80GB

Metric

NVIDIA RTX 4060 Ti 16GB

FP16 TFLOPS

989 T
44 T

Bandwidth

3.4k GB/s
288 GB/s

VRAM

803 GB
166 GB

TDP (lower=better)

700 W
165 W

TFLOPS/watt

1.41
0.27

GB/s per watt

4.79
1.75

Full specification table

SpecNVIDIA H100 SXM5 80GBNVIDIA RTX 4060 Ti 16GB
ArchitectureHopperAda Lovelace
VRAM80 GB HBM316 GB GDDR6
VRAM typeHBM3GDDR6
Memory bandwidth3,350 GB/s288 GB/s
FP16 TFLOPS989 TFLOPS44.2 TFLOPS
TDP700 W165 W
TFLOPS/watt1.41 T/W0.27 T/W
GB/s per watt4.79 GB/s/W1.75 GB/s/W
NVLinkYesNo
Release year20222023

Live cloud pricing

On-demand pricing across providers — updated in real time

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Power efficiency analysis

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

NVIDIA H100 SXM5 80GB

FP16 TFLOPS989 TFLOPS
TDP700 W
TFLOPS/watt1.413 T/W
GB/s per watt4.79 GB/s/W

NVIDIA RTX 4060 Ti 16GB

FP16 TFLOPS44.2 TFLOPS
TDP165 W
TFLOPS/watt0.268 T/W
GB/s per watt1.75 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 SXM5 80GB for:

  • LLM training
  • Multi-GPU NVLink clusters
  • High-throughput inference

Weaknesses / Limitations

  • Highest cost per H100 variant — premium over PCIe form factor
  • SXM5 form factor requires NVLink-capable server chassis
  • 700W TDP demands high-density power and cooling infrastructure

Choose NVIDIA RTX 4060 Ti 16GB for:

  • Budget inference
  • Small model fine-tuning
  • Entry Ada AI

Weaknesses / Limitations

  • Low bandwidth (288 GB/s) — inference throughput bottleneck
  • 16 GB VRAM ceiling — cannot run 34B+ models
  • Consumer GPU — no ECC memory or enterprise support