NVIDIA · GPU · Results: 26Q3, recorded 2026-09-25
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 123.13 |
| MobileNetV3 | BF16 | 69.72 |
| Flan-T5 Small | FP32 | 128.73 |
| Flan-T5 Small | BF16 | 55.85 |
| DistilGPT2 | FP32 | 132.96 |
| DistilGPT2 | BF16 | 77.45 |
| DistilBERT | FP32 | 173.42 |
| DistilBERT | BF16 | 130.37 |
| GraphGPS (Peptides) | FP32 | 49.58 |
| GraphGPS (Peptides) | BF16 | 43.52 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 353.03 |
| MobileNetV3 | INT8 | 56.05 |
| Flan-T5 Small | FP16 | 404.53 |
| Flan-T5 Small | INT8 | 179.00 |
| Flan-T5 Small | FP8 | 220.64 |
| DistilGPT2 | FP16 | 265.89 |
| DistilGPT2 | INT8 | 147.43 |
| DistilGPT2 | FP8 | 157.39 |
| DistilBERT | FP16 | 286.62 |
| DistilBERT | INT8 | 154.63 |
| DistilBERT | FP8 | 182.87 |
| GraphGPS (Peptides) | FP16 | 97.31 |
| GraphGPS (Peptides) | INT8 | 45.30 |
| GraphGPS (Peptides) | FP8 | 48.63 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 80.72 |
| Dense MatMul | FP16 | 86.13 |
| Dense MatMul | BF16 | 87.23 |
| Dense MatMul | FP64 | 2.38 |
| Sparse MatMul | FP32 | 234.35 |
| Sparse MatMul | FP64 | 174.12 |
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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