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Ada Lovelace

NVIDIA RTX 4060 8GB

wins

0

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

Turing

NVIDIA T4

wins

5

The NVIDIA T4 is the most widely deployed inference GPU in the cloud.

Low-cost inferenceEdge AIMulti-tenant serving

Performance comparison

Visual bars — winner highlighted

NVIDIA RTX 4060 8GB

Metric

NVIDIA T4

FP16 TFLOPS

30 T
65 T

Bandwidth

272 GB/s
320 GB/s

VRAM

86 GB
166 GB

TDP (lower=better)

115 W
70 W

TFLOPS/watt

0.26
0.93

GB/s per watt

2.37
4.57

Full specification table

SpecNVIDIA RTX 4060 8GBNVIDIA T4
ArchitectureAda LovelaceTuring
VRAM8 GB GDDR616 GB GDDR6
VRAM typeGDDR6GDDR6
Memory bandwidth272 GB/s320 GB/s
FP16 TFLOPS30.2 TFLOPS65 TFLOPS
TDP115 W70 W
TFLOPS/watt0.26 T/W0.93 T/W
GB/s per watt2.37 GB/s/W4.57 GB/s/W
NVLinkNoNo
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 T4

FP16 TFLOPS65 TFLOPS
TDP70 W
TFLOPS/watt0.929 T/W
GB/s per watt4.57 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 T4 for:

  • Low-cost inference
  • Edge AI
  • Multi-tenant serving

Weaknesses / Limitations

  • 16 GB VRAM limits model size to ~13B at INT4
  • Low FP16 TFLOPS (65) — not suitable for throughput-sensitive serving
  • Older Turing architecture — no Ada or Hopper efficiency improvements