H100 vs B200 for LLM training: 2026 benchmark and cost analysis
Side-by-side benchmark of NVIDIA H100 vs B200 for training 7B–70B parameter LLMs in 2026 — throughput, $/TFLOP, memory bandwidth, and when each chip wins.
If you are training a 7B–70B parameter LLM in 2026, the practical choice is almost always NVIDIA H100 vs NVIDIA B200. Both ship in 8-GPU SXM nodes; both are available across CoreWeave, AWS, Azure, Lambda and RunPod. The interesting question is not which is faster — B200 wins — but which is faster per dollar for your specific workload.
Headline benchmark
- 8x H100 SXM5 node: ~7.9 PFLOPS dense FP8, 640 GB HBM3, ~$18–$25/hr depending on provider.
- 8x B200 node: ~9.6 PFLOPS dense FP8, 1,536 GB HBM3e, ~$36–$42/hr.
- Per-PFLOP/hour: H100 ≈ $2.85, B200 ≈ $3.95. H100 still wins on raw $/TFLOP.
- B200 wins decisively on memory-bound workloads (KV-cache heavy, 70B+ context) where its 1.5 TB HBM3e collapses tensor-parallel comms.
When H100 is the right call
Stick with 8x H100 SXM5 when the model fits comfortably in 80 GB per device with reasonable batch sizes, when availability matters more than peak speed, and when the budget is tight. H100 capacity in EU-West-1 on RunPod and Lambda routinely lands under $20/hr — the cheapest credible LLM-training tier.
When B200 pays for itself
Pick B200 when memory pressure is the bottleneck: 70B+ dense models, long-context fine-tuning, or MoE architectures where expert weights blow past 80 GB. B200's HBM3e + NVLink 5 fabric reduces tensor-parallel chatter, turning the headline ~20% FLOPS bump into a 30–40% wall-clock win on training jobs that were memory-bound on H100.
The honest take
For most teams shipping production fine-tunes today, an 8x H100 SXM5 cluster on Lambda or CoreWeave remains the default. Reserve B200 capacity for the next-gen training run that actually exercises HBM3e — paying Blackwell prices to train a 7B model is a budgeting error.