NVIDIA · GPU · Results: 26Q3, recorded 2026-09-29
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 113.22 |
| MobileNetV3 | BF16 | 84.66 |
| Flan-T5 Small | FP32 | 120.25 |
| Flan-T5 Small | BF16 | 64.74 |
| DistilGPT2 | FP32 | 98.36 |
| DistilGPT2 | BF16 | 63.43 |
| DistilBERT | FP32 | 132.29 |
| DistilBERT | BF16 | 105.77 |
| GraphGPS (Peptides) | FP32 | 39.80 |
| GraphGPS (Peptides) | BF16 | 40.90 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 365.84 |
| MobileNetV3 | INT8 | 100.37 |
| Flan-T5 Small | FP16 | 304.88 |
| Flan-T5 Small | INT8 | 169.75 |
| Flan-T5 Small | FP8 | 205.70 |
| DistilGPT2 | FP16 | 200.54 |
| DistilGPT2 | INT8 | 128.30 |
| DistilGPT2 | FP8 | 125.43 |
| DistilBERT | FP16 | 226.96 |
| DistilBERT | INT8 | 136.64 |
| DistilBERT | FP8 | 142.44 |
| GraphGPS (Peptides) | FP16 | 92.03 |
| GraphGPS (Peptides) | INT8 | 46.56 |
| GraphGPS (Peptides) | FP8 | 47.11 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 50.46 |
| Dense MatMul | FP16 | 59.51 |
| Dense MatMul | BF16 | 60.09 |
| Dense MatMul | FP64 | 1.62 |
| Sparse MatMul | FP32 | 155.55 |
| Sparse MatMul | FP64 | 146.47 |
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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