Comparison

NVIDIA GB200 vs NVIDIA H100

Top-end Blackwell vs the industry workhorse

The GB200 delivers approximately 1.8x the compute with 2.4x the memory (192GB vs 80GB), but at a significant price premium. The H100 remains the most widely deployed training GPU.

Pricing Comparison

Specifications

Specification	NVIDIA GB200	NVIDIA H100
Manufacturer	NVIDIA	NVIDIA
Architecture	Blackwell	Hopper
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	192 GB	80 GB
FP16 Performance	1800 TFLOPS	990 TFLOPS
TDP	1000W	700W
Perf per Watt	1.80 TFLOPS/W	1.41 TFLOPS/W

Detailed Analysis

The GB200 represents NVIDIA's high-end Blackwell configuration, pairing two B200 dies in a single module. Compared to the H100, it offers a dramatic upgrade in both compute and memory.

The GB200's 192GB of HBM3e memory enables training of larger models without multi-GPU configurations, and its Blackwell architecture includes second-generation Transformer Engine support. However, its 1,000W TDP per module requires substantial power and cooling infrastructure.

The H100 remains the pragmatic choice for most organisations. Its 700W TDP, wide availability across all cloud regions, and mature CUDA ecosystem make it the default for production AI workloads. The GB200 is primarily targeting hyperscale deployments and frontier model training.

Verdict

Best for Training

GB200 for frontier-scale training. H100 for mainstream production training.

Best for Inference

H100 is more cost-effective for inference at most scales.

Best Value

H100 offers significantly better value for most workloads.

Frequently Asked Questions

Is the GB200 worth the premium over H100?

Only for very large-scale training workloads where the GB200's extra memory and compute significantly reduce training time. For most production workloads, the H100 offers better value.