Blackwell
NVIDIA GB200 NVL72
wins
4
The GB200 NVL72 is NVIDIA's most powerful AI system.
Ampere
NVIDIA RTX 3080 10GB
wins
1
The RTX 3080 is an entry-level option for AI workloads with 10GB GDDR6X.
Performance comparison
Visual bars — winner highlighted
NVIDIA GB200 NVL72
Metric
NVIDIA RTX 3080 10GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA GB200 NVL72 | NVIDIA RTX 3080 10GB |
|---|---|---|
| Architecture | Blackwell | Ampere |
| VRAM | 384 GB HBM3e (per GPU) | 10 GB GDDR6X |
| VRAM type | HBM3e (per GPU) | GDDR6X |
| Memory bandwidth | 16,000 GB/s | 760 GB/s |
| FP16 TFLOPS | 9,000 TFLOPS | 45 TFLOPS |
| TDP | 2,700 W | 320 W |
| TFLOPS/watt | 3.33 T/W | 0.14 T/W |
| GB/s per watt | 5.93 GB/s/W | 2.38 GB/s/W |
| NVLink | Yes | No |
| Release year | 2025 | 2020 |
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 GB200 NVL72
NVIDIA RTX 3080 10GB
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 GB200 NVL72 for:
- Trillion-parameter training
- AI factory scale
- Next-gen LLM inference
Weaknesses / Limitations
- Rack-scale system — not a single GPU, requires full NVL72 rack deployment
- 2,700W system TDP demands dedicated power and liquid cooling infrastructure
- Extremely limited availability — primarily hyperscaler and large enterprise
Choose NVIDIA RTX 3080 10GB for:
- Entry-level AI
- Small model inference
- Hobbyist compute
Weaknesses / Limitations
- 10 GB VRAM — only fits very small quantized models
- Older Ampere architecture with lower TFLOPS than Ada equivalents
- Not suitable for fine-tuning or production inference workloads
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