diff --git a/src/brevitas/graph/equalize.py b/src/brevitas/graph/equalize.py
index a9c492b97..e096171fb 100644
--- a/src/brevitas/graph/equalize.py
+++ b/src/brevitas/graph/equalize.py
@@ -28,6 +28,7 @@
 __all__ = ['GraphActivationEqualization', 'LayerwiseActivationEqualization', 'EqualizeGraph']
 
 EPSILON = 1e-9
+FLOAT16_EPSILON = 1e-4
 
 _supported_layers = (
     nn.ConvTranspose1d,
@@ -334,6 +335,7 @@ def _cross_layer_equalization(
     # Determine device and type of tensors
     device = next(sinks[0].parameters()).device
     dtype = next(sinks[0].parameters()).dtype
+    epsilon = FLOAT16_EPSILON if dtype == torch.float16 else EPSILON
 
     # If equalization criteria are not met, we return a scalar one to indicate that no equalization
     # has been performed
@@ -398,7 +400,7 @@ def _no_equalize():
     scale_fn = _select_scale_computation_fn(scale_computation_type)
     sink_weights = [transpose(m, axis) for m, axis in sink_axes.items()]
     sinks_range = scale_fn(torch.cat([w.reshape(w.size(0), -1) for w in sink_weights], 1))
-    sinks_range = torch.clamp(sinks_range, EPSILON)
+    sinks_range = torch.clamp(sinks_range, epsilon)
 
     # Determine the srcs_range based on where we are performing activation equalization or
     # weight equalization
@@ -434,7 +436,7 @@ def _no_equalize():
     srcs_range = torch.pow(srcs_range, alpha)
     sinks_range = torch.pow(sinks_range, 1 - alpha)
     scaling_factors = srcs_range / sinks_range
-    scaling_factors = torch.clamp(scaling_factors, EPSILON)
+    scaling_factors = torch.clamp(scaling_factors, epsilon)
     inverse_scaling_factors = torch.reciprocal(scaling_factors)
 
     if list_of_act_val is not None and list_of_insert_mul_node_fn is not None: