TensorRT INT8 vs FP16 on RF-DETR, Measured on an RTX 5090
Originally published on khadim.tech . In short: On an RTX 5090 with TensorRT 10.12, a calibrated INT8 build of RF-DETR put zero layers in INT8. TensorRT's own timing chose FP16 for every layer, so the INT8 engine was the FP16 engine: about 5x faster than the PyTorch FP32 forward, with no extra gain…
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- 2026-10-23 21:01 · DEV Community — Machine Learning
TensorRT INT8 vs FP16 on RF-DETR, Measured on an RTX 5090