NVIDIA · GPU · Results: 26Q3, recorded 2026-09-25
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 198.59 |
| MobileNetV3 | BF16 | 147.87 |
| Flan-T5 Small | FP32 | 214.24 |
| Flan-T5 Small | BF16 | 129.44 |
| DistilGPT2 | FP32 | 202.74 |
| DistilGPT2 | BF16 | 125.27 |
| DistilBERT | FP32 | 255.66 |
| DistilBERT | BF16 | 204.93 |
| GraphGPS (Peptides) | FP32 | 71.33 |
| GraphGPS (Peptides) | BF16 | 77.84 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 732.42 |
| MobileNetV3 | INT8 | 207.89 |
| Flan-T5 Small | FP16 | 557.15 |
| Flan-T5 Small | INT8 | 326.40 |
| Flan-T5 Small | FP8 | 412.59 |
| DistilGPT2 | FP16 | 390.25 |
| DistilGPT2 | INT8 | 247.62 |
| DistilGPT2 | FP8 | 247.53 |
| DistilBERT | FP16 | 421.72 |
| DistilBERT | INT8 | 255.90 |
| DistilBERT | FP8 | 285.20 |
| GraphGPS (Peptides) | FP16 | 170.74 |
| GraphGPS (Peptides) | INT8 | 82.53 |
| GraphGPS (Peptides) | FP8 | 85.78 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 102.23 |
| Dense MatMul | FP16 | 113.86 |
| Dense MatMul | BF16 | 114.71 |
| Dense MatMul | FP64 | 3.27 |
| Sparse MatMul | FP32 | 262.76 |
| Sparse MatMul | FP64 | 288.62 |
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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