Blackwell
NVIDIA GB200 NVL72
wins
4
The GB200 NVL72 is NVIDIA's most powerful AI system.
Hopper
NVIDIA H100 80GB
wins
1
The NVIDIA H100 is the gold standard for AI training and inference. With 80GB HBM3 memory and NVLink 4.0 support, it dominates large-model training and multi-GPU clusters.
Performance comparison
Visual bars — winner highlighted
NVIDIA GB200 NVL72
Metric
NVIDIA H100 80GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | NVIDIA GB200 NVL72 | NVIDIA H100 80GB |
|---|---|---|
| Architecture | Blackwell | Hopper |
| VRAM | 384 GB HBM3e (per GPU) | 80 GB HBM3 |
| VRAM type | HBM3e (per GPU) | HBM3 |
| Memory bandwidth | 16,000 GB/s | 3,350 GB/s |
| FP16 TFLOPS | 9,000 TFLOPS | 989 TFLOPS |
| TDP | 2,700 W | 700 W |
| TFLOPS/watt | 3.33 T/W | 1.41 T/W |
| GB/s per watt | 5.93 GB/s/W | 4.79 GB/s/W |
| NVLink | Yes | Yes |
| Release year | 2025 | 2022 |
Live cloud pricing
On-demand pricing across providers — updated in real time
NVIDIA GB200 NVL72
No live pricing data available.
NVIDIA H100 80GB
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA GB200 NVL72
NVIDIA H100 80GB
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.
Performance-per-dollar
TFLOPS/$ and GB/s/$ at current best cloud prices
NVIDIA GB200 NVL72
NVIDIA H100 80GB
TFLOPS/$ = FP16 TFLOPS ÷ best on-demand $/hr. Higher is better — more compute per dollar. GB/s/$ = memory bandwidth ÷ best on-demand $/hr. For memory-bound inference workloads (most LLM serving), GB/s/$ is often a better predictor of cost-efficiency than TFLOPS/$. Prices are live from the Compute Comparison API and update in real time.
Workload Cost Comparison
Total job cost = best on-demand $/hr × GPU count × hours
| Workload | Scale | NVIDIA GB200 NVL72 | NVIDIA H100 80GB |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | — | $547 |
| 70B LLM fine-tune | 64× GPU, 72 h | — | $13k |
| Inference server | 1× GPU, 730 h | — | $2.1k |
| Image gen batch | 4× GPU, 8 h | — | $91 |
Based on current best on-demand prices. Spot pricing typically 40–70% lower. Actual cost depends on provider, region, and availability.
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 H100 80GB for:
- LLM training
- Large-scale inference
- HPC workloads
Weaknesses / Limitations
- Highest rental cost of any widely available GPU
- SXM form factor requires NVLink-capable server chassis
- 700W TDP demands high-density power and cooling infrastructure
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