diff --git a/src/brevitas/quant_tensor/float_quant_tensor.py b/src/brevitas/quant_tensor/float_quant_tensor.py
index cf4ba1420..ecb2c7ae2 100644
--- a/src/brevitas/quant_tensor/float_quant_tensor.py
+++ b/src/brevitas/quant_tensor/float_quant_tensor.py
@@ -103,8 +103,9 @@ def _pre_round_float_value(self):
             scale = self.scale.type(torch.float32)
         minifloat_value = value / scale
         fp_internal_scale = 1. - self.exponent_bias - self.mantissa_bit_width
+        eps = torch.finfo(self.scale.dtype).tiny
         int_scale = float_internal_scale(
-            self.value, self.mantissa_bit_width, fp_internal_scale, self.eps)
+            self.value / self.scale, self.mantissa_bit_width, fp_internal_scale, eps)
         minifloat_value = minifloat_value / int_scale
         return minifloat_value
 
@@ -140,8 +141,9 @@ def minifloat(self, float_datatype=True):
 
         if self.is_valid:
             fp_internal_scale = 1. - self.exponent_bias - self.mantissa_bit_width
+            eps = torch.finfo(self.scale.dtype).tiny
             int_scale = float_internal_scale(
-                self.value, self.mantissa_bit_width, fp_internal_scale, self.eps)
+                self.value / self.scale, self.mantissa_bit_width, fp_internal_scale, eps)
             float_value = torch.round(self._pre_round_float_value) * int_scale
             return float_value.type(self.scale.dtype)
         else:
diff --git a/src/brevitas/quant_tensor/groupwise_float_quant_tensor.py b/src/brevitas/quant_tensor/groupwise_float_quant_tensor.py
index fa91bdca1..9b703e0f7 100644
--- a/src/brevitas/quant_tensor/groupwise_float_quant_tensor.py
+++ b/src/brevitas/quant_tensor/groupwise_float_quant_tensor.py
@@ -144,7 +144,9 @@ def _pre_round_float_value(self):
             scale = scale.type(torch.float32)
         minifloat_value = value / scale
         fp_internal_scale = 1. - self.exponent_bias - self.mantissa_bit_width
-        int_scale = float_internal_scale(self.value, self.mantissa_bit_width, fp_internal_scale)
+        eps = torch.finfo(self.scale_.dtype).tiny
+        int_scale = float_internal_scale(
+            self.value / self.scale, self.mantissa_bit_width, fp_internal_scale, eps)
         minifloat_value = minifloat_value / int_scale
         return minifloat_value
 
@@ -180,7 +182,9 @@ def minifloat(self, float_datatype=True):
 
         if self.is_valid:
             fp_internal_scale = 1. - self.exponent_bias - self.mantissa_bit_width
-            int_scale = float_internal_scale(self.value, self.mantissa_bit_width, fp_internal_scale)
+            eps = torch.finfo(self.scale_.dtype).tiny
+            int_scale = float_internal_scale(
+                self.value / self.scale, self.mantissa_bit_width, fp_internal_scale, eps)
             float_value = torch.round(self._pre_round_float_value) * int_scale
             return float_value.type(self.scale.dtype)
         else:
diff --git a/tests/brevitas/quant_tensor/test_quant_tensor.py b/tests/brevitas/quant_tensor/test_quant_tensor.py
index 4e3401057..6f6b4c7d2 100644
--- a/tests/brevitas/quant_tensor/test_quant_tensor.py
+++ b/tests/brevitas/quant_tensor/test_quant_tensor.py
@@ -4,11 +4,14 @@
 
 from packaging import version
 import pytest
+import pytest_cases
 import torch
 
 from brevitas import torch_version
 from brevitas.nn import QuantIdentity
+from brevitas.quant.experimental.float import Fp8e4m3ActPerTensorFloat
 from brevitas.quant.experimental.float_quant_ocp import Fp8e5m2OCPActPerTensorFloat
+from brevitas.quant.experimental.mx_quant_ocp import MXFloat8e4m3Act
 from brevitas.quant_tensor import FloatQuantTensor
 from brevitas.quant_tensor import IntQuantTensor
 
@@ -119,3 +122,19 @@ def test_quant_tensor_view():
     assert torch.allclose(a.view(2, -1), b.view(2, -1), atol=0.01)
     assert torch.allclose(a.view(16, -1), b.view(16, -1), atol=0.01)
     assert torch.allclose(a.view(8, 2), b.view(8, 2), atol=0.01)
+
+
+QUANT_CLASS = {'fp8': Fp8e4m3ActPerTensorFloat, 'mxfp8': MXFloat8e4m3Act}
+
+
+@pytest_cases.parametrize('quant_class_key_vale', QUANT_CLASS.items())
+def test_minifloat(quant_class_key_vale):
+    key, quant_class = quant_class_key_vale
+
+    x = torch.randn((1, 32))
+    q = QuantIdentity(quant_class, group_dim=-1, return_quant_tensor=True)
+    q.eval()
+
+    qx = q(x)
+    # Check that minifloat doesn't raise error
+    qx.minifloat()