diff --git a/optimum/amd/brevitas/configuration.py b/optimum/amd/brevitas/configuration.py
index b5f4e637..8cd2dba1 100644
--- a/optimum/amd/brevitas/configuration.py
+++ b/optimum/amd/brevitas/configuration.py
@@ -137,5 +137,8 @@ def __post_init__(self):
             self.activations_group_size = None
             self.activations_param_method = None
 
+    def add_bias_to_linear(self):
+        return self.apply_bias_correction and self.device == "auto"
+
     def requires_fx_graph(self):
         return self.activations_equalization == "cross_layer" or self.apply_weight_equalization
diff --git a/optimum/amd/brevitas/quantizer.py b/optimum/amd/brevitas/quantizer.py
index d06e143b..88e7ecb6 100644
--- a/optimum/amd/brevitas/quantizer.py
+++ b/optimum/amd/brevitas/quantizer.py
@@ -192,6 +192,8 @@ def quantize(
         if use_accelerate:
             remove_hooks(model)
             device = None
+            if quantization_config.add_bias_to_linear():
+                model = add_zero_bias_to_linear(model)
         else:
             device = next(model.parameters()).device
 
@@ -244,6 +246,7 @@ def quantize(
             apply_bias_correction(
                 model,
                 calibration_dataset,
+                skip_if_no_bias=use_accelerate,  # We can't add keys to the state dict if accelerate is being used
             )
             logger.info("Bias Correction applied.")
 
@@ -331,7 +334,21 @@ def apply_calibration(model: torch.nn.Module, dataset: List[Dict]) -> None:
 
 
 @torch.no_grad()
-def apply_bias_correction(model: torch.nn.Module, dataset: List[Dict]) -> None:
-    with bias_correction_mode(model):
+def apply_bias_correction(model: torch.nn.Module, dataset: List[Dict], skip_if_no_bias: bool = False) -> None:
+    with bias_correction_mode(model, skip_if_no_bias=skip_if_no_bias):
         for inps in tqdm(dataset):
             model(**inps)
+
+
+@torch.no_grad()
+def add_zero_bias_to_linear(model: torch.nn.Module) -> torch.nn.Module:
+    for name, module in model.named_modules():
+        if type(module) == torch.nn.Linear:
+            if module.bias is None:
+                module.register_parameter(
+                    "bias",
+                    torch.nn.Parameter(
+                        torch.zeros((module.weight.shape[0],), device=module.weight.device, dtype=module.weight.dtype)
+                    ),
+                )
+    return model