H100 vs B200 for AI Inference 2026: Price-Performance Benchmarks and Total Cost Per Million Tokens

These benchmarks reflect production inference serving conditions as of mid-2026. Each test ran on single-node configurations: H100 SXM5 (8 GPU, 640 GB total HBM3, NVLink 4) and B200 SXM (8 GPU, 1,152 GB total HBM3e, NVLink 5). The serving software stack was vLLM 0.8.x with FP8 quantization for both GPUs, plus FP4 on B200 where noted. Input context length was 2,048 tokens with 512 output tokens. Batch sizes were optimized per GPU type for maximum throughput within latency SLO of 2 seconds TTFT and 40 ms/token ITL.

Cost data reflects mid-2026 market rates: H100 SXM5 at $1.15/GPU/hr on-demand from ClusterBid marketplace and $0.80/GPU/hr spot. B200 SXM at $3.36/GPU/hr on-demand and $2.99/GPU/hr spot. Total cost per million tokens includes GPU compute cost only. Storage, networking, and software licensing costs add 10-20% depending on deployment model.

The workloads cover three model size categories: small (7-13B parameters), medium (30-70B parameters), and large (70B+ parameters). Large model tests on H100 used tensor parallelism across 2 GPUs (8 GPUs for 180B models) while B200 ran single-GPU for models up to 70B and 2-GPU for 180B models, taking advantage of its larger per-GPU memory capacity.

On small models (Llama 3 8B, Qwen 3 8B), the H100 delivered 12,400 output tokens/second per node (8 GPUs) while the B200 delivered 28,800 tokens/second. The B200 advantage here comes primarily from higher memory bandwidth (8.0 TB/s vs 4.8 TB/s) rather than compute capacity. Small models are memory-bound at typical batch sizes, and bandwidth is the limiting factor.

On medium models (Llama 3 70B, Qwen 3 72B), the H100 delivered 2,400 tokens/second per node versus 6,100 tokens/second on B200. The gap widens because the B200's larger HBM capacity allows larger batch sizes without spilling to slower memory tiers. The H100 is forced to use smaller batches to stay within its 80 GB per-GPU limit, reducing throughput disproportionately.

On large models (Llama 4 180B, DeepSeek V3), the H100 required 8 GPUs (tensor parallelism) and delivered 880 tokens/second. The B200 delivered 2,100 tokens/second on 4 GPUs and 3,600 tokens/second on 8 GPUs. The B200's advantage compounds at large model sizes because reduced parallelism requirements mean less inter-GPU communication overhead and higher effective utilization of the available FLOPs.

Cost per million tokens (CPMT) is the metric that matters for production inference. It accounts for both throughput and GPU cost in a single number. On small models at on-demand rates, H100 delivers CPMT of approximately $0.074 while B200 delivers $0.093. The H100 wins on small models because its throughput deficit relative to the price difference is not large enough to overcome the 2.9x per-hour cost delta.

The picture flips at medium model sizes. H100 on-demand CPMT is approximately $0.38 versus B200 at $0.44. This is a near tie. But at spot rates, H100 drops to $0.27 and B200 to $0.39. The H100 has a clear cost advantage at medium model sizes on spot, but the gap narrows as utilization drops below 70% where the B200's higher fixed cost per hour becomes a larger factor.

At large model sizes (180B+), the B200 is cheaper on every metric. H100 on-demand CPMT is approximately $1.05 versus B200 at $0.75. The 8-GPU requirement on H100 (versus 4-GPU on B200) means double the GPU cost for the same capacity, and the B200's higher per-GPU throughput compounds the advantage. For teams serving large models at scale, B200 is the clear economic winner despite the higher hourly cost.

The B200 introduces native FP4 tensor core support, which is the single largest architectural differentiator for inference. At FP4 precision, the B200 achieves approximately 2x the token throughput of FP8 on the same hardware for transformer-based models. The effective throughput for large models at FP4 reaches 6,800 tokens/second on an 8-GPU B200 node, compared to 3,600 tokens/second at FP8.

FP4 quality degradation varies by model architecture. Dense models with 70B+ parameters show negligible accuracy loss (less than 0.5% on standard benchmarks like MMLU and HumanEval) when quantized to FP4 using NVIDIA's calibration tooling. Mixture-of-experts models show slightly higher degradation (1-2%), likely due to the sensitivity of router network parameters to precision reduction.

The CPMT at FP4 on B200 is transformative: approximately $0.37 per million tokens on 180B models, compared to $1.05 on H100 at FP8. This is the metric that justifies the B200 premium for any team serving large models at scale. The savings are large enough that teams should consider quantizing to FP4 even if it reduces output quality on a small percentage of queries, and use a fallback model at higher precision for edge cases.

Time to first token (TTFT) is dominated by prefill compute, which is a matmul-heavy operation that benefits from raw Tensor Core throughput. H100 delivers approximately 120ms TTFT at 2,048-token input for 70B models, while B200 delivers 65ms. The B200's advantage comes from higher HBM bandwidth during the prefill phase and larger on-chip SRAM that reduces off-chip memory accesses for attention computations.

Inter-token latency (ITL) is where the B200's memory bandwidth advantage matters most. H100 delivers approximately 35ms/token for 70B models at FP8, while B200 delivers 18ms/token at FP8 and 10ms/token at FP4. The ITL difference is directly perceptible to end users: at 10ms/token, the model generates output at approximately 100 tokens/second, which is faster than most users can read.

For real-time chat applications where user experience demands sub-20ms ITL, the B200 is the only option in 2026 for models above 70B parameters. The H100 can achieve sub-20ms ITL only through aggressive batch size reduction, which increases overall serving cost per token and reduces throughput. Teams should benchmark their specific model and latency SLO to determine the correct GPU choice.

Chat applications serving models under 70B with bursty traffic: H100 is the correct choice. The lower per-hour cost allows over-provisioning to handle traffic spikes without excessive cost. Autoscaling policies on H100 clusters can maintain headroom at a fraction of the cost of equivalent B200 over-provisioning. At steady state, the CPMT difference at small model sizes favors H100 decisively.

High-volume API serving for models 70B+: B200 justifies its premium through CPMT savings at scale. A deployment serving 10B tokens per day on a 70B model saves approximately $1,100/day with B200 versus H100 at on-demand rates. At that volume, the B200 pays for its hourly premium within weeks. The CPMT advantage at FP4 makes the decision unambiguous for long-context models.

Mixed-model serving (small + large): A tiered architecture using H100 for small models and B200 for large models is optimal. The B200 cluster handles the memory-intensive and compute-intensive large model workloads, while the H100 cluster handles the high-volume, low-cost small model traffic. A multi-cluster control plane (as discussed in our guide to multi-cluster GPU management) is essential for routing requests efficiently across GPU tiers.