NVIDIA's B200 (Blackwell architecture) delivers ~2x the training performance of H100, with 192GB HBM3e memory and 8 TB/s bandwidth. Expected pricing is $10-15/hr on-demand, 3-4x H100 rates. B200 availability is expected late 2026. For organizations that can wait, B200 offers significantly better price/performance for training workloads.
| Specification | NVIDIA H100 SXM | NVIDIA B200 SXM | Improvement |
|---|---|---|---|
| Architecture | Hopper | Blackwell | Next-gen |
| GPU Memory | 80 GB HBM3 | 192 GB HBM3e | +140% |
| Memory Bandwidth | 3.35 TB/s | 8.0 TB/s | +139% |
| FP8 Performance | 3,958 TFLOPS | ~9,000 TFLOPS | +127% |
| LLM Training Speed | 1.0x (baseline) | ~2.0x | 2x faster |
| TDP | 700W | 1000W | +43% |
| On-Demand Lease Rate | $2.50 - $3.50/hr | $10 - $15/hr (est.) | +3-4x |
| Availability | Good | Late 2026 | - |
B200 specifications are based on NVIDIA announcements. Pricing is estimated based on market trends.
FP4 precision support enables 2x performance for inference while maintaining accuracy for production LLMs.
1.8 TB/s GPU-to-GPU bandwidth enables efficient 8-GPU configurations without performance bottlenecks.
Hardware decompression accelerates data loading, critical for training on large datasets.
Major cloud providers (CoreWeave, Lambda Labs) are expected to offer B200 instances in late 2026, with hyperscalers (AWS, GCP, Azure) following in early 2027. Limited early access programs may be available sooner.
Yes, B200 is CUDA-compatible with H100. Your existing training and inference code will work without modification. New features like FP4 require code changes to leverage.
B100 is the lower-power variant (700W) with less memory (80GB). B200 is the flagship with 192GB and 1000W TDP. For training workloads, B200 is preferred; B100 is better for power-constrained inference.
If your timeline allows, yes. B200 will offer better price/performance than H200. However, if you need memory (141GB+) before late 2026, H200 is the only option available.
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