NVIDIA · GPU · Results: 26Q3, recorded 2026-09-23
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 356.54 |
| MobileNetV3 | BF16 | 229.48 |
| Flan-T5 Small | FP32 | 424.46 |
| Flan-T5 Small | BF16 | 257.49 |
| DistilGPT2 | FP32 | 348.25 |
| DistilGPT2 | BF16 | 198.02 |
| DistilBERT | FP32 | 443.76 |
| DistilBERT | BF16 | 372.87 |
| GraphGPS (Peptides) | FP32 | 152.72 |
| GraphGPS (Peptides) | BF16 | 163.11 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 1598.29 |
| MobileNetV3 | INT8 | 254.00 |
| Flan-T5 Small | FP16 | 1018.11 |
| Flan-T5 Small | INT8 | 558.02 |
| Flan-T5 Small | FP8 | 673.82 |
| DistilGPT2 | FP16 | 572.39 |
| DistilGPT2 | INT8 | 343.60 |
| DistilGPT2 | FP8 | 336.92 |
| DistilBERT | FP16 | 733.41 |
| DistilBERT | INT8 | 455.48 |
| DistilBERT | FP8 | 482.05 |
| GraphGPS (Peptides) | FP16 | 293.75 |
| GraphGPS (Peptides) | INT8 | 144.79 |
| GraphGPS (Peptides) | FP8 | 148.59 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 194.65 |
| Dense MatMul | FP16 | 190.03 |
| Dense MatMul | BF16 | 186.38 |
| Dense MatMul | FP64 | 5.81 |
| Sparse MatMul | FP32 | 141.84 |
| Sparse MatMul | FP64 | 105.29 |
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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