NVIDIA · GPU · Results: 26Q3, recorded 2026-09-24
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 259.27 |
| MobileNetV3 | BF16 | 168.48 |
| Flan-T5 Small | FP32 | 301.17 |
| Flan-T5 Small | BF16 | 173.32 |
| DistilGPT2 | FP32 | 241.45 |
| DistilGPT2 | BF16 | 126.99 |
| DistilBERT | FP32 | 301.49 |
| DistilBERT | BF16 | 240.58 |
| GraphGPS (Peptides) | FP32 | 108.46 |
| GraphGPS (Peptides) | BF16 | 127.59 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 1049.65 |
| MobileNetV3 | INT8 | 290.72 |
| Flan-T5 Small | FP16 | 637.45 |
| Flan-T5 Small | INT8 | 407.72 |
| Flan-T5 Small | FP8 | 465.40 |
| DistilGPT2 | FP16 | 358.91 |
| DistilGPT2 | INT8 | 214.06 |
| DistilGPT2 | FP8 | 211.46 |
| DistilBERT | FP16 | 518.04 |
| DistilBERT | INT8 | 323.68 |
| DistilBERT | FP8 | 325.27 |
| GraphGPS (Peptides) | FP16 | 206.04 |
| GraphGPS (Peptides) | INT8 | 103.82 |
| GraphGPS (Peptides) | FP8 | 97.60 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 114.63 |
| Dense MatMul | FP16 | 118.23 |
| Dense MatMul | BF16 | 119.90 |
| Dense MatMul | FP64 | 3.53 |
| Sparse MatMul | FP32 | 85.90 |
| Sparse MatMul | FP64 | 61.05 |
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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