NVIDIA · GPU · Results: 26Q3, recorded 2026-09-06
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP32 | 223.41 |
| MobileNetV3 | BF16 | 167.57 |
| Flan-T5 Small | FP32 | 217.83 |
| Flan-T5 Small | BF16 | 84.44 |
| DistilGPT2 | FP32 | 235.19 |
| DistilGPT2 | BF16 | 127.25 |
| DistilBERT | FP32 | 327.44 |
| DistilBERT | BF16 | 240.08 |
| GraphGPS (Peptides) | FP32 | 87.06 |
| GraphGPS (Peptides) | BF16 | 66.17 |
| Model | Precision | Sustained |
|---|---|---|
| MobileNetV3 | FP16 | 544.80 |
| MobileNetV3 | INT8 | 195.64 |
| Flan-T5 Small | FP16 | 571.92 |
| Flan-T5 Small | INT8 | 325.97 |
| Flan-T5 Small | FP8 | 359.41 |
| DistilGPT2 | FP16 | 378.96 |
| DistilGPT2 | INT8 | 260.25 |
| DistilGPT2 | FP8 | 318.18 |
| DistilBERT | FP16 | 400.41 |
| DistilBERT | INT8 | 154.17 |
| DistilBERT | FP8 | 336.30 |
| GraphGPS (Peptides) | FP16 | 197.16 |
| GraphGPS (Peptides) | INT8 | 65.48 |
| GraphGPS (Peptides) | FP8 | 92.08 |
| Model | Precision | Sustained |
|---|---|---|
| Dense MatMul | FP32 | 128.68 |
| Dense MatMul | FP16 | 124.81 |
| Dense MatMul | BF16 | 164.35 |
| Dense MatMul | FP64 | 4.69 |
| Sparse MatMul | FP32 | 289.06 |
| Sparse MatMul | FP64 | 420.56 |
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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