Comparison

NVIDIA H200 vs NVIDIA B200

Memory-optimised Hopper vs Blackwell

The B200 has 1.8x the compute (1,800 vs 990 TFLOPS) with more memory (192GB vs 141GB). The H200 offers more memory per dollar in some deployments.

Pricing Comparison

Specifications

Specification	NVIDIA H200	NVIDIA B200
Manufacturer	NVIDIA	NVIDIA
Architecture	Hopper	Blackwell
Accelerator Type	GPU	GPU
Primary Use	training	training
Memory (VRAM)	141 GB	192 GB
FP16 Performance	990 TFLOPS	1800 TFLOPS
TDP	700W	1000W
Perf per Watt	1.41 TFLOPS/W	1.80 TFLOPS/W

Detailed Analysis

The H200 and B200 represent different generational approaches. The H200 is a memory-upgraded H100 (Hopper), while the B200 is a clean-sheet Blackwell design.

The B200's compute advantage (1,800 vs 990 TFLOPS) is substantial and impacts training speed directly. Its 192GB of HBM3e also exceeds the H200's 141GB, though the gap is smaller than the compute difference.

The H200's advantage is ecosystem maturity and availability. Being based on the proven Hopper architecture, it benefits from the same software optimisations as the H100. The B200, while more capable, is still in earlier deployment stages.

For inference-heavy workloads where memory bandwidth matters more than raw compute, the H200 can still compete due to its strong 4.8 TB/s memory bandwidth. For training workloads, the B200's compute advantage makes it the clear winner.

Verdict

Best for Training

B200 — 1.8x more compute makes a significant difference for training time.

Best for Inference

H200 can be competitive for memory-bound inference. B200 wins for compute-bound serving.

Best Value

H200 offers good value for memory-heavy inference. B200 for maximum capability.

Frequently Asked Questions

Is the H200 obsolete now that B200 exists?

No. The H200's memory capacity (141GB) and proven Hopper ecosystem make it relevant for memory-bound inference workloads, often at a lower cost than the B200.