NVIDIA · GPU · Results: 26Q3, recorded 2026-09-29
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 310.18 |
| MobileNetV3 | BF16 | 198.01 |
| Flan-T5 Small | FP32 | 293.79 |
| Flan-T5 Small | BF16 | 177.96 |
| DistilGPT2 | FP32 | 310.23 |
| DistilGPT2 | BF16 | 201.45 |
| DistilBERT | FP32 | 354.46 |
| DistilBERT | BF16 | 313.77 |
| GraphGPS (Peptides) | FP32 | 104.18 |
| GraphGPS (Peptides) | BF16 | 127.40 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 1063.14 |
| MobileNetV3 | INT8 | 293.36 |
| Flan-T5 Small | FP16 | 714.42 |
| Flan-T5 Small | INT8 | 450.98 |
| Flan-T5 Small | FP8 | 0.00 |
| DistilGPT2 | FP16 | 548.28 |
| DistilGPT2 | INT8 | 315.46 |
| DistilGPT2 | FP8 | 0.00 |
| DistilBERT | FP16 | 596.19 |
| DistilBERT | INT8 | 374.63 |
| DistilBERT | FP8 | 0.00 |
| GraphGPS (Peptides) | FP16 | 303.62 |
| GraphGPS (Peptides) | INT8 | 152.54 |
| GraphGPS (Peptides) | FP8 | 0.00 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 116.91 |
| Dense MatMul | FP16 | 144.52 |
| Dense MatMul | BF16 | 144.53 |
| Dense MatMul | FP64 | 4.10 |
| Sparse MatMul | FP32 | 100.53 |
| Sparse MatMul | FP64 | 75.06 |
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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