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Blackwell Ultra B300 vs Hopper H200 SXM

Complete side-by-side comparison of specs, performance, memory, power efficiency, and pricing.

NVIDIA

Blackwell Ultra B300

100

Spec Wins

NVIDIA

Hopper H200 SXM

74

Detailed Specifications

SpecBlackwell Ultra B300Hopper H200 SXM
ArchitectureBlackwell Ultra Hopper
Memory288GB HBM3e 141GB HBM3e
Memory Bandwidth12,000 GB/s 4,800 GB/s
FP16 TFLOPS3,500 1,979
FP8 TFLOPS7,000 3,958
BF16 TFLOPS3,500 1,979
INT8 TOPS14,000 3,958
TDP1400W 700W
InterconnectNVLink 5.0 (1800 GB/s) (1800 GB/s) NVLink 4.0 (900 GB/s) (900 GB/s)
Perf Score100 74
EcosystemCUDA CUDA
Est. Price$40,000 $30,000

Blackwell Ultra B300 — Best For

Trillion-Parameter TrainingAGI ResearchSovereign AI

Hopper H200 SXM — Best For

LLM InferenceLarge Models

Who Should Choose Each GPU?

Choose Blackwell Ultra B300 if you…

  • Need maximum CUDA/TensorRT/vLLM ecosystem compatibility
  • Need more VRAM (288GB vs 141GB) for large model inference
  • Prioritize raw FP8 throughput (7,000 vs 3,958 TFLOPS)
  • Running Trillion-Parameter Training workloads
  • Running AGI Research workloads
  • Running Sovereign AI workloads

Choose Hopper H200 SXM if you…

  • Need maximum CUDA/TensorRT/vLLM ecosystem compatibility
  • Have power-constrained data centers (700W vs 1400W TDP)
  • Working with a tighter CapEx budget (lower list price)
  • Running LLM Inference workloads
  • Running Large Models workloads

Verdict

The Blackwell Ultra B300 and Hopper H200 SXM target different priorities. The Blackwell Ultra B300's 288GB of HBM3e gives it a clear edge for large-model inference where fitting the full model in VRAM eliminates quantization overhead. For training throughput, the Blackwell Ultra B300's 7,000 FP8 TFLOPS outpaces the Hopper H200 SXM's 3,958 TFLOPS. Both GPUs use CUDA, so ecosystem switching cost is not a factor. Use our TCO Calculator to model the full 3-year cost difference for your specific utilization and power costs.

Blackwell Ultra B300 vs Hopper H200 SXM: Common Questions

Which is faster, Blackwell Ultra B300 or Hopper H200 SXM?+

In FP8 throughput, the Blackwell Ultra B300 leads with 7,000 TFLOPS vs 3,958 TFLOPS. For LLM inference, memory capacity and bandwidth often matter more than raw TFLOPS — the Blackwell Ultra B300 has more VRAM (288GB).

Is Blackwell Ultra B300 or Hopper H200 SXM better for LLM training?+

For LLM training at scale, the Blackwell Ultra B300 has higher raw throughput. However, the choice also depends on your software stack: Blackwell Ultra B300 offers CUDA compatibility with the widest framework support (PyTorch, JAX, TensorRT).

What is the price difference between Blackwell Ultra B300 and Hopper H200 SXM?+

The Blackwell Ultra B300 is estimated at $40,000 per unit and the Hopper H200 SXM at $30,000. Actual pricing varies by vendor, volume, and configuration. Check our Buy page for current reseller pricing.

Which GPU is more power efficient, Blackwell Ultra B300 or Hopper H200 SXM?+

The Hopper H200 SXM has a lower TDP (700W vs 1400W). Performance-per-watt depends on your workload — for FP8 inference, divide TFLOPS by TDP: Blackwell Ultra B300 = 5.0 TFLOPS/W vs Hopper H200 SXM = 5.7 TFLOPS/W.

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