Blackwell
NVIDIA B200 180GB
wins
5
The B200 is NVIDIA's Blackwell flagship — 4.5 PFLOPS FP16 and 8 TB/s memory bandwidth make it the most powerful GPU available for AI training at scale.
Hopper
NVIDIA H200 141GB
wins
1
The H200 upgrades the H100 with 141GB HBM3e memory and 4.8 TB/s bandwidth — a massive boost for memory-bound workloads like large LLM inference.
Performance comparison
Visual bars — winner highlighted
NVIDIA B200 180GB
Metric
NVIDIA H200 141GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
TFLOPS/$
FP16 TFLOPS ÷ best on-demand $/hr
GB/s per $
Memory bandwidth ÷ best on-demand $/hr
Full specification table
| Spec | NVIDIA B200 180GB | NVIDIA H200 141GB |
|---|---|---|
| Architecture | Blackwell | Hopper |
| VRAM | 180 GB HBM3e | 141 GB HBM3e |
| VRAM type | HBM3e | HBM3e |
| Memory bandwidth | 8,000 GB/s | 4,800 GB/s |
| FP16 TFLOPS | 4,500 TFLOPS | 989 TFLOPS |
| TDP | 1,000 W | 700 W |
| TFLOPS/watt | 4.50 T/W | 1.41 T/W |
| GB/s per watt | 8.00 GB/s/W | 6.86 GB/s/W |
| NVLink | Yes | Yes |
| Release year | 2024 | 2024 |
| Best cloud price | $3.50/hr | $2.61/hr |
| TFLOPS/$ | 1285.7 | 378.9 |
| GB/s per $ | 2286 | 1839 |
Live cloud pricing
On-demand pricing across providers — updated in real time
NVIDIA B200 180GB
NVIDIA H200 141GB
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
NVIDIA B200 180GB
NVIDIA H200 141GB
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 B200 180GB
NVIDIA H200 141GB
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
NVIDIA B200 180GB costs 1.3× more at current best prices. 1.7× higher memory bandwidth (8000 vs 4800 GB/s) improves token throughput for memory-bound inference; 4.6× the FP16 compute (4500 vs 989 TFLOPS) supports faster training and higher batch throughput.
| Workload | Scale | NVIDIA B200 180GB | NVIDIA H200 141GB |
|---|---|---|---|
| 7B LLM fine-tune | 8× GPU, 24 h | $672 | $501▼ |
| 70B LLM fine-tune | 64× GPU, 72 h | $16k | $12k▼ |
| Inference server | 1× GPU, 730 h | $2.6k | $1.9k▼ |
| Image gen batch | 4× GPU, 8 h | $112 | $84▼ |
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 B200 180GB for:
- Next-gen LLM training
- Trillion-parameter models
- AI factories
Weaknesses / Limitations
- Extremely limited availability — primarily reserved for hyperscalers
- 1,000W TDP requires specialized power and liquid cooling
- Highest cost per GPU of any currently available card
Choose NVIDIA H200 141GB for:
- Very large LLMs
- Memory-bound inference
- Multi-modal models
Weaknesses / Limitations
- Very limited cloud availability — fewer providers than H100
- Premium pricing over H100 with same FP16 TFLOPS
- 700W TDP — same power envelope as H100 with no efficiency gain
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