diff --git a/torchinfo/torchinfo.py b/torchinfo/torchinfo.py
index c0b6bb5b..e54ba68c 100644
--- a/torchinfo/torchinfo.py
+++ b/torchinfo/torchinfo.py
@@ -17,6 +17,7 @@
 
 import numpy as np
 import torch
+from packaging import version
 from torch import nn
 from torch.jit import ScriptModule
 from torch.utils.hooks import RemovableHandle
@@ -473,8 +474,17 @@ def get_device(
     If input_data is given, the device should not be changed
     (to allow for multi-device models, etc.)
 
-    Otherwise gets device of first parameter of model and returns it if it is on cuda,
-    otherwise returns cuda if available or cpu if not.
+    Otherwise gets device of first parameter of model and returns it.
+    As #371 mentioned, in system with other accelerator (such as MPS),
+    returns the accelerator (introduced in pytorch 2.6.0) device if available,
+    otherwise returns cuda if cuda is available, if not then return cpu.
+
+    Attempting to also add DirectML device (may include AMD GPU support).
+    However since personally didn't have any AMD GPU at the moment,
+    the implementation is solely based on documentation and examples.
+    DirectML documentation and example:
+    https://github.com/microsoft/DirectML
+    https://medium.com/@ochwada/preparations-for-pytorch-and-directml-on-amd-ryzen-9-6950h-for-ai-projects-15c164d22332
     """
     if input_data is None:
         try:
@@ -482,9 +492,26 @@ def get_device(
         except StopIteration:
             model_parameter = None
 
-        if model_parameter is not None and model_parameter.is_cuda:
+        if model_parameter is not None and model_parameter.device:
             return model_parameter.device
-        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # Since torch.accelerator is available in torch 2.6.0 and above
+        if version.parse(torch.__version__) >= version.parse("2.6.0"):
+            try:
+                accelerator_module = getattr(torch, "accelerator", None)
+                if accelerator_module is not None:
+                    if accelerator_module.is_available():
+                        return accelerator_module.current_accelerator()  # type: ignore[no-any-return]
+            except Exception as e:
+                print(f"Error getting accelerator device: {e}")
+        try:
+            import torch_directml  # type: ignore[import-not-found]
+
+            print("Pytorch DirectML is installed, using DirectML device.")
+            if model_parameter is not None:
+                return torch_directml.device(torch_directml.default_device())  # type: ignore[no-any-return]
+        except ImportError:
+            print("Pytorch DirectML is not installed.")
+        return torch.device("cpu")
     return None