Compute Comparison
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Ada Lovelace

NVIDIA RTX 4060 8GB

wins

1

The RTX 4060 is the most power-efficient Ada Lovelace GPU at 115W TDP. 8GB GDDR6 limits it to small quantized models (up to ~7B at INT4).

Ultra-budget inferenceSmall model experimentationEntry-level Ada AI

Volta

NVIDIA V100 32GB

wins

4

The V100 was NVIDIA's flagship AI GPU before the A100.

Legacy ML trainingBudget HPCMature framework support

Performance comparison

Visual bars — winner highlighted

NVIDIA RTX 4060 8GB

Metric

NVIDIA V100 32GB

FP16 TFLOPS

30 T
125 T

Bandwidth

272 GB/s
900 GB/s

VRAM

86 GB
322 GB

TDP (lower=better)

115 W
300 W

TFLOPS/watt

0.26
0.42

GB/s per watt

2.37
3.00

Full specification table

SpecNVIDIA RTX 4060 8GBNVIDIA V100 32GB
ArchitectureAda LovelaceVolta
VRAM8 GB GDDR632 GB HBM2
VRAM typeGDDR6HBM2
Memory bandwidth272 GB/s900 GB/s
FP16 TFLOPS30.2 TFLOPS125 TFLOPS
TDP115 W300 W
TFLOPS/watt0.26 T/W0.42 T/W
GB/s per watt2.37 GB/s/W3.00 GB/s/W
NVLinkNoYes
Release year20232018

Live cloud pricing

On-demand pricing across providers — updated in real time

Per hourPer GB VRAM/hr
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Power efficiency analysis

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

NVIDIA RTX 4060 8GB

FP16 TFLOPS30.2 TFLOPS
TDP115 W
TFLOPS/watt0.263 T/W
GB/s per watt2.37 GB/s/W

NVIDIA V100 32GB

FP16 TFLOPS125 TFLOPS
TDP300 W
TFLOPS/watt0.417 T/W
GB/s per watt3.00 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 RTX 4060 8GB for:

  • Ultra-budget inference
  • Small model experimentation
  • Entry-level Ada AI

Weaknesses / Limitations

  • 8 GB VRAM — only fits quantized models up to ~7B parameters
  • Very low bandwidth (272 GB/s) — severe bottleneck for inference throughput
  • Not viable for fine-tuning beyond tiny models

Choose NVIDIA V100 32GB for:

  • Legacy ML training
  • Budget HPC
  • Mature framework support

Weaknesses / Limitations

  • Volta architecture — significantly lower TFLOPS than A100 or H100
  • 32 GB VRAM ceiling limits modern LLM workloads
  • No BF16 support — less efficient for modern transformer training