Why B200s Are Cheaper for Large-Scale Training Than H100s

For large-scale AI training, the cheapest GPU per hour isn't the cheapest per training run. When you factor in training speed, NVIDIA's B200 actually costs less than the H100 for serious workloads. Here's why.

The Price Paradox

On-Demand GPU Pricing (per hour):

H100 pricing shown is for multinode configurations (2+ nodes). TensorPool charges $1.99/hr for single-node H100s.

B200s cost 2x more per hour than multinode H100s. But cost per training run is what matters.

Why B200s Train Faster

B200 Advantages:

• 2.4x more memory (192 GB vs 80 GB) = 3x larger batch sizes
• 2.4x memory bandwidth (8 TB/s vs 3.35 TB/s) = faster data movement
• Native FP6/FP4 training = 2-4x faster with quantization-aware training
• Dual-die architecture = better scaling for large models

Conservative estimate: B200s complete training runs in 50-60% of the time H100s take when using FP8 or lower precision training.

Real-World Training Costs

Training a 70B LLM (30 days on 64 H100s)

H100 Setup:

• 64 GPUs for 30 days (720 hours)
• Total: 46,080 GPU-hours
• Cost: $114,739

B200 Setup (with 2x speedup from FP8 + larger batches):

• 64 GPUs for 15 days (360 hours)
• Total: 23,040 GPU-hours
• Cost: $114,970

Analysis: Nearly identical cost! When you can use the same number of GPUs, the 2x speedup almost perfectly offsets B200's 2x higher hourly rate. B200 completes in half the time (15 vs 30 days).

Training a 175B LLM

This is where B200s shine. A 175B model requires:

H100 Setup:

• 128 GPUs minimum (memory constraints)
• 90 days (2,160 hours) estimated
• Total: 276,480 GPU-hours
• Cost: $688,435

B200 Setup (2x speedup + half the GPUs due to 2.4x memory):

• 64 GPUs (B200's 192 GB fits more model)
• 45 days (1,080 hours)
• Total: 69,120 GPU-hours
• Cost: $344,909

Winner: B200 saves $343,526 (50%) and completes in half the wall-clock time with half the infrastructure.

When B200s Actually Win

B200s deliver better economics when memory capacity is the bottleneck:

1. Training Models >150B Parameters You need fewer B200s than H100s due to 2.4x memory capacity. Fewer GPUs × faster training > higher hourly cost.

2. Serving Large Models B200's 2.4x bandwidth directly improves inference throughput. For production serving, throughput per dollar matters more than cost per hour.

3. Quantized Training Workflows If you're using FP4/FP6 quantization-aware training (which B200 supports natively), the 3-4x speedup makes B200s significantly cheaper per training run.

4. Multi-Modal Models Vision-language models with high memory requirements benefit from 192 GB capacity, reducing node count.

The Real Math: When Does B200 Break Even?

B200 becomes cost-effective when:

Memory-driven savings > hourly rate premium

Example calculation:

• If B200's 192 GB lets you use 64 GPUs instead of 128 H100s:

  • H100 cost: 128 GPUs × $2.49/hr = $318.72/hr
  • B200 cost: 64 GPUs × $4.99/hr = $319.36/hr

• But if B200 is 2x faster:

  • H100 total: $318.72/hr × 100 hours = $31,872
  • B200 total: $319.36/hr × 50 hours = $15,968
  • B200 saves 50%

The formula: B200 wins when (H100_GPU_count / B200_GPU_count) × speedup > (B200_price / H100_price))

For a 175B model: (128 / 64) × 2.0 = 4.0 > 2.0 ✓

When to Use Each GPU

Choose H100 for:

• Models <100B parameters where memory isn't limiting GPU count
• When you can fully utilize available GPUs (no memory bottleneck)
• Budget-constrained research with smaller models
• Fine-tuning with LoRA/QLoRA
• Inference serving for smaller models (<40B parameters)

Choose B200 for:

• Training models >150B parameters
• Quantized training workflows (FP4/FP6 QAT)
• When memory capacity limits your H100 cluster size
• Production inference at scale
• Long-context applications (64K+ tokens)

Conclusion

At $2.49/hr for multinode H100s vs $4.99/hr for B200s, the economics are surprisingly balanced:

When GPU counts are equal: B200's 2x speedup almost perfectly offsets the 2x higher cost. You pay the same but finish in half the time.

When memory limits GPU count: B200 delivers massive savings. Training a 175B model costs 50% less on B200s because you need half as many GPUs.

The deciding factors:

• Time-to-market matters? Choose B200 for 2x faster completion.
• Memory-constrained workload? Choose B200 for 30-50% cost savings.
• Can use same GPU count? Either works—B200 finishes faster, H100 costs slightly less.

For models >150B parameters, B200s are clearly superior: lower cost, faster training, simpler infrastructure.

Bottom line: At multinode pricing, B200s are competitive even when GPU counts match, and dramatically cheaper when memory is the bottleneck.

Note: Speedup estimates assume FP8 quantization-aware training with optimized batch sizes. Actual performance varies by model architecture, framework, and cluster configuration. Pricing from October 2025.