NVIDIA · GPU · LynxBenchAI 26Q3 Index, recorded 2026-10-28
On the LynxBenchAI 26Q3 Index, the NVIDIA H100 NVL ranks 9 of 48 devices overall with a GT of 692.67 (Training 588.50, rank 8; Inference 1123.94, rank 9; Compute 347.75, rank 11); overall it scores 0.74x the NVIDIA H100 80GB HBM3 and 1.54x the NVIDIA GeForce RTX 4090.
Hopper architecture · 94 GB HBM3 · PCIe card (NVIDIA specifications)
Rank among all 48 devices; further right is higher.
| Compared with | Overall | Training | Inference | Compute |
|---|---|---|---|---|
| NVIDIA H100 80GB HBM3 | 0.74x | 0.83x | 0.67x | 0.71x |
| NVIDIA GeForce RTX 4090 | 1.54x | 1.75x | 1.70x | 0.95x |
| Score | Next above | Ratio | Next below | Ratio |
|---|---|---|---|---|
| Training | NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 0.87x | NVIDIA RTX PRO 6000 Blackwell Server Edition | 1.00x |
| Inference | NVIDIA RTX PRO 6000 Blackwell Server Edition | 0.94x | NVIDIA H100 PCIe | 1.08x |
| Compute | NVIDIA RTX PRO 6000 Blackwell Server Edition | 0.95x | NVIDIA L40S | 1.01x |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 537.67 |
| MobileNetV3 | BF16 | 243.47 |
| Flan-T5 Small | FP32 | 898.75 |
| Flan-T5 Small | BF16 | 523.52 |
| DistilGPT2 | FP32 | 903.48 |
| DistilGPT2 | BF16 | 522.66 |
| DistilBERT | FP32 | 1081.01 |
| DistilBERT | BF16 | 940.17 |
| GraphGPS (Peptides) | FP32 | 318.48 |
| GraphGPS (Peptides) | BF16 | 357.80 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 2187.52 |
| MobileNetV3 | INT8 | 692.80 |
| Flan-T5 Small | FP16 | 2327.68 |
| Flan-T5 Small | INT8 | 947.28 |
| Flan-T5 Small | FP8 | 1356.97 |
| DistilGPT2 | FP16 | 2084.27 |
| DistilGPT2 | INT8 | 954.30 |
| DistilGPT2 | FP8 | 1194.18 |
| DistilBERT | FP16 | 1714.39 |
| DistilBERT | INT8 | 720.85 |
| DistilBERT | FP8 | 912.88 |
| GraphGPS (Peptides) | FP16 | 748.98 |
| GraphGPS (Peptides) | INT8 | 341.34 |
| GraphGPS (Peptides) | FP8 | 368.62 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 706.80 |
| Dense MatMul | FP16 | 696.38 |
| Dense MatMul | BF16 | 715.56 |
| Dense MatMul | FP64 | 240.90 |
| Sparse MatMul | FP32 | 143.02 |
| Sparse MatMul | FP64 | 254.53 |
LynxBenchAI 26Q3 Index · GT = LynxBenchAI Global Score. Every precision result is sustained throughput under a declared, bounded optimisation budget — no collapsed single number below the category level.
Each figure is the first device's overall score divided by the second's.
A LynxBenchAI score is sustained throughput on a fixed catalogue of PyTorch models, measured after a warm-up at a batch size raised until throughput stops improving, so it is what the machine holds under load, not its best instant. Training, Inference and Compute are three separate scores; GT combines them (Training and Inference weigh 0.4 each, Compute 0.2).
Every run uses the stock, publicly released PyTorch stack, so a result is broadly indicative of the out-of-the-box PyTorch experience on that hardware, and so of custom PyTorch models too. It does not run your own model, and it is not a vendor-tuned peak like an MLPerf submission.
Scores compare only within one Index: the 26Q3 Index fixes the models, precisions, correctness thresholds and scoring, and a later Index may change them. Each test is one timed window, the scores have no 0-100 scale, and a precision a device cannot run scores zero rather than being skipped.
LynxBenchAI is built by TechnoLynx, which the European Commission engaged as an independent expert advisor on AI performance and benchmarking for AI Gigafactories under the EuroHPC Joint Undertaking (case study).
Measure your own NVIDIA H100 NVL: pip install lynxbench-ai · Install guide →
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