diff --git a/src/transformers/quantizers/quantizer_torchao.py b/src/transformers/quantizers/quantizer_torchao.py
index 21dc2498d9a..5b23a6173d8 100644
--- a/src/transformers/quantizers/quantizer_torchao.py
+++ b/src/transformers/quantizers/quantizer_torchao.py
@@ -277,6 +277,45 @@ class TorchAoHfQuantizer(HfQuantizer):
             return False
         return _is_torchao_serializable
 
+    def get_cuda_warm_up_factor(self):
+        """
+        This factor is used in caching_allocator_warmup to determine how many bytes to pre-allocate for CUDA warmup.
+        - A factor of 2 means we pre-allocate the full memory footprint of the model.
+        - A factor of 4 means we pre-allocate half of that, and so on
+
+        However, when using TorchAO, calculating memory usage with param.numel() * param.element_size() doesn't give the correct size for quantized weights (like int4 or int8)
+        That's because TorchAO internally represents quantized tensors using subtensors and metadata, and the reported element_size() still corresponds to the torch_dtype
+        not the actual bit-width of the quantized data.
+
+        To correct for this:
+        - Use a division factor of 8 for int4 weights
+        - Use a division factor of 4 for int8 weights
+        """
+        if self.quantization_config._get_ao_version() > version.Version("0.9.0"):
+            from torchao.core.config import AOBaseConfig
+
+            quant_type = self.quantization_config.quant_type
+            # For autoquant case, it will be treated in the string implementation below in map_to_target_dtype
+            if isinstance(quant_type, AOBaseConfig):
+                # Extract size digit using fuzzy match on the class name
+                config_name = quant_type.__class__.__name__
+                size_digit = fuzzy_match_size(config_name)
+
+                if size_digit == "4":
+                    return 8
+                else:
+                    return 4
+
+        # Original mapping for non-AOBaseConfig types
+        map_to_target_dtype = {
+            "int4_weight_only": 8,
+            "int8_weight_only": 4,
+            "int8_dynamic_activation_int8_weight": 4,
+            "autoquant": 4,
+        }
+
+        return map_to_target_dtype[self.quantization_config.quant_type]
+
     @property
     def is_trainable(self) -> bool:
         supported_quant_types_for_training = [