diff --git a/tests/brevitas/graph/test_calibration.py b/tests/brevitas/graph/test_calibration.py
index 86ef58b77..b22994275 100644
--- a/tests/brevitas/graph/test_calibration.py
+++ b/tests/brevitas/graph/test_calibration.py
@@ -4,6 +4,7 @@
 import math
 
 from hypothesis import given
+import pytest_cases
 from pytest_cases import fixture
 import torch
 import torch.nn as nn
@@ -13,14 +14,23 @@
 from brevitas.graph.calibrate import load_quant_model_mode
 import brevitas.nn as qnn
 from brevitas.quant import Int8ActPerTensorFixedPoint
+from brevitas.quant.experimental.float import Fp8e4m3ActPerTensorFloat
 from brevitas.quant.scaled_int import Int8ActPerTensorFloat
 # Use custom implementation of kthvalue as work around to (b)float16 kernel limitations
 from brevitas.utils.torch_utils import kthvalue
 from tests.brevitas.hyp_helper import float_tensor_random_size_st
+from tests.conftest import SEED
 
+torch.manual_seed(SEED)
 IN_CH = 8
 OUT_CH = 16
 BATCH = 1
+REFERENCE_SCALES = {
+    'int_quant': (0.00935234408825635910, 0.01362917013466358185),
+    'fp_quant': (0.00249395845457911491, 0.00363444536924362183)}
+REFERENCE_INP = torch.tensor([[-1.8645, -0.4071, 1.1971]])
+REFERENCE_WEIGHTS = torch.tensor([[1.0023, 0.0205, 1.4604], [-0.2918, -1.8218, -0.7010],
+                                  [1.4573, -0.9074, -0.2708]])
 
 
 def compute_quantile(x, q):
@@ -65,6 +75,41 @@ def forward(self, x):
     assert torch.allclose(expected_scale, scale)
 
 
+QUANTS = {'int_quant': Int8ActPerTensorFloat, 'fp_quant': Fp8e4m3ActPerTensorFloat}
+
+
+@pytest_cases.parametrize("act_quant", QUANTS.items(), ids=QUANTS.keys())
+def test_scale_factors_ptq_calibration_reference(act_quant):
+
+    reference, act_quant = act_quant
+
+    class TestModel(nn.Module):
+
+        def __init__(self):
+            super(TestModel, self).__init__()
+            self.act = qnn.QuantReLU(act_quant=act_quant)
+            self.linear_weights = REFERENCE_WEIGHTS
+            self.act_1 = qnn.QuantIdentity(act_quant=act_quant)
+
+        def forward(self, x):
+            o = self.act(x)
+            o = torch.matmul(o, self.linear_weights)
+            return self.act_1(o)
+
+    # Reference input
+    inp = REFERENCE_INP
+    model = TestModel()
+    model.eval()
+    with torch.no_grad():
+        with calibration_mode(model):
+            model(inp)
+
+    computed_scale = model.act.act_quant.scale(), model.act_1.act_quant.scale()
+    reference_values = REFERENCE_SCALES[reference]
+    assert torch.allclose(computed_scale[0], torch.tensor(reference_values[0]))
+    assert torch.allclose(computed_scale[1], torch.tensor(reference_values[1]))
+
+
 def test_calibration_training_state():
 
     class TestModel(nn.Module):