// Cross-posted from Easy Diffusion’s blog. Experimented with TensorRT-RTX (a new library offered by NVIDIA). The first step was a tiny toy model, just to get the build and test setup working. The reference model in PyTorch: import torch import torch.nn as nn class TinyCNN(nn.Module): def __init__(self): super().__init__() self.conv = nn.Conv2d(3, 8, 3, stride=1, padding=1) self.relu = nn.ReLU() self.pool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(8, 4) # 4-class toy output def forward(self, x): x = self.relu(self.conv(x)) x = self.pool(x).flatten(1) return self.fc(x)I ran this on a NVIDIA 4060 8 GB (Laptop) for 10K iterations, on Windows and WSL-with-Ubuntu, with float32 data.