NVIDIA · GPU · Results: 26Q3, recorded 2026-09-06
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 289.97 |
| MobileNetV3 | BF16 | 181.03 |
| Flan-T5 Small | FP32 | 268.15 |
| Flan-T5 Small | BF16 | 163.34 |
| DistilGPT2 | FP32 | 276.90 |
| DistilGPT2 | BF16 | 182.47 |
| DistilBERT | FP32 | 319.49 |
| DistilBERT | BF16 | 283.27 |
| GraphGPS (Peptides) | FP32 | 95.92 |
| GraphGPS (Peptides) | BF16 | 115.98 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 961.23 |
| MobileNetV3 | INT8 | 266.79 |
| Flan-T5 Small | FP16 | 625.54 |
| Flan-T5 Small | INT8 | 401.95 |
| Flan-T5 Small | FP8 | 0.00 |
| DistilGPT2 | FP16 | 474.79 |
| DistilGPT2 | INT8 | 273.58 |
| DistilGPT2 | FP8 | 0.00 |
| DistilBERT | FP16 | 520.47 |
| DistilBERT | INT8 | 324.84 |
| DistilBERT | FP8 | 0.00 |
| GraphGPS (Peptides) | FP16 | 279.26 |
| GraphGPS (Peptides) | INT8 | 142.39 |
| GraphGPS (Peptides) | FP8 | 0.00 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 133.30 |
| Dense MatMul | FP16 | 128.71 |
| Dense MatMul | BF16 | 130.00 |
| Dense MatMul | FP64 | 3.82 |
| Sparse MatMul | FP32 | 89.86 |
| Sparse MatMul | FP64 | 64.19 |
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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