NVIDIA · GPU · Results: 26Q3, recorded 2026-09-06
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 346.28 |
| MobileNetV3 | BF16 | 173.31 |
| Flan-T5 Small | FP32 | 579.06 |
| Flan-T5 Small | BF16 | 336.21 |
| DistilGPT2 | FP32 | 609.72 |
| DistilGPT2 | BF16 | 343.98 |
| DistilBERT | FP32 | 736.08 |
| DistilBERT | BF16 | 639.53 |
| GraphGPS (Peptides) | FP32 | 212.64 |
| GraphGPS (Peptides) | BF16 | 214.16 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 1472.07 |
| MobileNetV3 | INT8 | 459.72 |
| Flan-T5 Small | FP16 | 1411.06 |
| Flan-T5 Small | INT8 | 672.11 |
| Flan-T5 Small | FP8 | 0.00 |
| DistilGPT2 | FP16 | 1390.64 |
| DistilGPT2 | INT8 | 630.82 |
| DistilGPT2 | FP8 | 0.00 |
| DistilBERT | FP16 | 1102.80 |
| DistilBERT | INT8 | 531.81 |
| DistilBERT | FP8 | 0.00 |
| GraphGPS (Peptides) | FP16 | 525.67 |
| GraphGPS (Peptides) | INT8 | 260.48 |
| GraphGPS (Peptides) | FP8 | 0.00 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 317.87 |
| Dense MatMul | FP16 | 384.97 |
| Dense MatMul | BF16 | 418.74 |
| Dense MatMul | FP64 | 117.84 |
| Sparse MatMul | FP32 | 92.16 |
| Sparse MatMul | FP64 | 163.89 |
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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