NVIDIA · GPU · Results: 26Q3, recorded 2026-09-25
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 338.31 |
| MobileNetV3 | BF16 | 226.33 |
| Flan-T5 Small | FP32 | 346.81 |
| Flan-T5 Small | BF16 | 205.83 |
| DistilGPT2 | FP32 | 301.44 |
| DistilGPT2 | BF16 | 180.52 |
| DistilBERT | FP32 | 400.90 |
| DistilBERT | BF16 | 305.83 |
| GraphGPS (Peptides) | FP32 | 129.29 |
| GraphGPS (Peptides) | BF16 | 162.59 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 1348.90 |
| MobileNetV3 | INT8 | 353.93 |
| Flan-T5 Small | FP16 | 909.89 |
| Flan-T5 Small | INT8 | 483.70 |
| Flan-T5 Small | FP8 | 656.48 |
| DistilGPT2 | FP16 | 633.84 |
| DistilGPT2 | INT8 | 410.40 |
| DistilGPT2 | FP8 | 412.17 |
| DistilBERT | FP16 | 679.07 |
| DistilBERT | INT8 | 459.23 |
| DistilBERT | FP8 | 507.14 |
| GraphGPS (Peptides) | FP16 | 237.93 |
| GraphGPS (Peptides) | INT8 | 120.89 |
| GraphGPS (Peptides) | FP8 | 120.37 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 193.11 |
| Dense MatMul | FP16 | 194.44 |
| Dense MatMul | BF16 | 193.72 |
| Dense MatMul | FP64 | 4.85 |
| Sparse MatMul | FP32 | 618.18 |
| Sparse MatMul | FP64 | 475.98 |
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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