Turing
NVIDIA T4
wins
2
The NVIDIA T4 is the most widely deployed inference GPU in the cloud.
Volta
NVIDIA V100 32GB
wins
3
The V100 was NVIDIA's flagship AI GPU before the A100.
Performance comparison
Visual bars — winner highlighted
NVIDIA T4
Metric
NVIDIA V100 32GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA T4 | NVIDIA V100 32GB |
|---|---|---|
| Architecture | Turing | Volta |
| VRAM | 16 GB GDDR6 | 32 GB HBM2 |
| VRAM type | GDDR6 | HBM2 |
| Memory bandwidth | 320 GB/s | 900 GB/s |
| FP16 TFLOPS | 65 TFLOPS | 125 TFLOPS |
| TDP | 70 W | 300 W |
| TFLOPS/watt | 0.93 T/W | 0.42 T/W |
| GB/s per watt | 4.57 GB/s/W | 3.00 GB/s/W |
| NVLink | No | Yes |
| Release year | 2018 | 2018 |
Live cloud pricing
On-demand pricing across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA T4
NVIDIA V100 32GB
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 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
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
Popular GPU comparisons