NVIDIA · GPU · Results: 26Q3, recorded 2026-10-06
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 217.79 |
| MobileNetV3 | BF16 | 150.47 |
| Flan-T5 Small | FP32 | 240.41 |
| Flan-T5 Small | BF16 | 131.34 |
| DistilGPT2 | FP32 | 283.93 |
| DistilGPT2 | BF16 | 162.03 |
| DistilBERT | FP32 | 368.16 |
| DistilBERT | BF16 | 290.50 |
| GraphGPS (Peptides) | FP32 | 88.52 |
| GraphGPS (Peptides) | BF16 | 89.45 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 682.36 |
| MobileNetV3 | INT8 | 190.06 |
| Flan-T5 Small | FP16 | 640.51 |
| Flan-T5 Small | INT8 | 286.79 |
| Flan-T5 Small | FP8 | 0.00 |
| DistilGPT2 | FP16 | 523.46 |
| DistilGPT2 | INT8 | 248.53 |
| DistilGPT2 | FP8 | 0.00 |
| DistilBERT | FP16 | 527.07 |
| DistilBERT | INT8 | 277.68 |
| DistilBERT | FP8 | 0.00 |
| GraphGPS (Peptides) | FP16 | 182.67 |
| GraphGPS (Peptides) | INT8 | 98.21 |
| GraphGPS (Peptides) | FP8 | 0.00 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 209.02 |
| Dense MatMul | FP16 | 144.11 |
| Dense MatMul | BF16 | 146.46 |
| Dense MatMul | FP64 | 3.48 |
| Sparse MatMul | FP32 | 84.89 |
| Sparse MatMul | FP64 | 58.80 |
Results: 26Q3 · GT = LynxBenchAI Global Score. Every precision result is sustained throughput under a declared, bounded optimisation budget — no collapsed single number below the category level.
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