Ada Lovelace
NVIDIA RTX 4090
wins
3
The RTX 4090 is the most cost-effective consumer GPU for AI workloads. At $0.40–$0.80/hr on GPU clouds, it's ideal for fine-tuning and small-model inference.
Ampere
NVIDIA RTX 3090
wins
1
The RTX 3090 remains a popular budget option for AI workloads with 24GB GDDR6X.
Performance comparison
Visual bars — winner highlighted
NVIDIA RTX 4090
Metric
NVIDIA RTX 3090
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA RTX 4090 | NVIDIA RTX 3090 |
|---|---|---|
| Architecture | Ada Lovelace | Ampere |
| VRAM | 24 GB GDDR6X | 24 GB GDDR6X |
| VRAM type | GDDR6X | GDDR6X |
| Memory bandwidth | 1,008 GB/s | 936 GB/s |
| FP16 TFLOPS | 165 TFLOPS | 71 TFLOPS |
| TDP | 450 W | 350 W |
| TFLOPS/watt | 0.37 T/W | 0.20 T/W |
| GB/s per watt | 2.24 GB/s/W | 2.67 GB/s/W |
| NVLink | No | No |
| Release year | 2022 | 2020 |
Live cloud pricing
On-demand $/hr across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA RTX 4090
NVIDIA RTX 3090
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 4090 for:
- Fine-tuning
- Small model inference
- Cost-effective compute
Choose NVIDIA RTX 3090 for:
- Budget fine-tuning
- Small model inference
- Hobbyist AI
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