If your model’s weights aren’t huge then perhaps you were already close to compute bound in the float version.

Sounds weird. I have model with completely stupid weights like {-1, 0, 1} and observe no performance gain after I apply --load-profile option.
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(3, 8, 3)
        self.conv2 = nn.Conv2d(8, 16, 3)
        self.conv3 = nn.Conv2d(16, 32, 3)
        self.conv4 = nn.Conv2d(32, 64, 3)
        self.conv5 = nn.Conv2d(64, 128, 3)
        self.conv6 = nn.Conv2d(128, 1000, 3)

        # Replce all weights by dummy 0 or 1
        entities = [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6]

        for e in entities:
            e.weight[:] = torch.randint(-1, 1, e.weight.size())
            e.bias[:]   = torch.randint(-1, 1, e.bias.size())

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv4(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv5(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv6(x)), (2, 2))
        x = x.view(16, 1000)
        return x

And collected profile
---
- nodeOutputName:  'learned_101:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_81:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_41:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_21:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'relu5:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A291:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'learned_9:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'zero5:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'relu4:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A261:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'learned_5:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'zero4:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'learned_11:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'A252:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'data:0'
  scale:           0.00392157
  offset:          -128
- nodeOutputName:  'learned_61:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_3:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_1:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'zero2:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A241:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'learned_7:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'relu2:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A201:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A222:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'A181:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A192:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'A211:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'relu:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A141:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A151:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'save_output:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A301:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A132:0'
  scale:           0.0705882
  offset:          127
- nodeOutputName:  'zero:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A162:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'A271:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A282:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'learned_01:0'
  scale:           0.00392157
  offset:          127
- nodeOutputName:  'A131:0'
  scale:           0.00392157
  offset:          -128
- nodeOutputName:  'output1:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'zero1:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'relu3:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A231:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'relu1:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'A171:0'
  scale:           0.1
  offset:          0
- nodeOutputName:  'zero3:0'
  scale:           0.1
  offset:          0
...


I do not believe that glow as AI compiler do not have any debugging tools which includes logs and some intermediate IR representations for observations and analysis.