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Blackwell

NVIDIA GB200 NVL72

wins

4

The GB200 NVL72 is NVIDIA's most powerful AI system.

Trillion-parameter trainingAI factory scaleNext-gen LLM inference

Ada Lovelace

NVIDIA RTX 6000 Ada

wins

1

The RTX 6000 Ada is NVIDIA's professional workstation GPU with 48GB GDDR6 and 364 TFLOPS FP16.

Professional inferenceFine-tuningWorkstation AI

Performance comparison

Visual bars — winner highlighted

NVIDIA GB200 NVL72

Metric

NVIDIA RTX 6000 Ada

FP16 TFLOPS

9.0k T
364 T

Bandwidth

16.0k GB/s
960 GB/s

VRAM

3.8k GB
486 GB

TDP (lower=better)

2.7k W
300 W

TFLOPS/watt

3.33
1.21

GB/s per watt

5.93
3.20

Full specification table

SpecNVIDIA GB200 NVL72NVIDIA RTX 6000 Ada
ArchitectureBlackwellAda Lovelace
VRAM384 GB HBM3e (per GPU)48 GB GDDR6
VRAM typeHBM3e (per GPU)GDDR6
Memory bandwidth16,000 GB/s960 GB/s
FP16 TFLOPS9,000 TFLOPS364 TFLOPS
TDP2,700 W300 W
TFLOPS/watt3.33 T/W1.21 T/W
GB/s per watt5.93 GB/s/W3.20 GB/s/W
NVLinkYesNo
Release year20252022

Live cloud pricing

On-demand pricing across providers — updated in real time

Per hourPer GB VRAM/hr

NVIDIA GB200 NVL72

No live pricing data available.

NVIDIA RTX 6000 Ada

QuantaCloud
$0.78/hr

Power efficiency analysis

TFLOPS/watt and GB/s/watt — critical for data center TCO

NVIDIA GB200 NVL72

FP16 TFLOPS9,000 TFLOPS
TDP2,700 W
TFLOPS/watt3.333 T/W
GB/s per watt5.93 GB/s/W

NVIDIA RTX 6000 Ada

FP16 TFLOPS364 TFLOPS
TDP300 W
TFLOPS/watt1.213 T/W
GB/s per watt3.20 GB/s/W

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

Best cloud priceN/A
TFLOPS per $—
GB/s per $—

NVIDIA RTX 6000 Ada

Best cloud price$0.78/hr
TFLOPS per $468.9 T/$
GB/s per $1237 GB/s/$

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

WorkloadScaleNVIDIA GB200 NVL72NVIDIA RTX 6000 Ada
7B LLM fine-tune8× GPU, 24 h—$149
70B LLM fine-tune64× GPU, 72 h—$3.6k
Inference server1× GPU, 730 h—$567
Image gen batch4× GPU, 8 h—$25

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 RTX 6000 Ada for:

  • Professional inference
  • Fine-tuning
  • Workstation AI

Weaknesses / Limitations

  • GDDR6 bandwidth lower than HBM-based data center GPUs
  • No NVLink on Ada generation
  • Workstation positioning means higher list price than L40S for similar specs