diff --git a/ingest.py b/ingest.py
index 5819e0f8..6508a445 100644
--- a/ingest.py
+++ b/ingest.py
@@ -1,5 +1,7 @@
 import logging
 import os
+import sys
+from utils import default_device_type
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
 
 import click
@@ -38,7 +40,7 @@ def load_single_document(file_path: str) -> Document:
        return loader.load()[0]
     except Exception as ex:
        file_log('%s loading error: \n%s' % (file_path, ex))
-       return None 
+       return None
 
 def load_document_batch(filepaths):
     logging.info("Loading document batch")
@@ -93,7 +95,7 @@ def load_documents(source_dir: str) -> list[Document]:
                 docs.extend(contents)
             except Exception as ex:
                 file_log('Exception: %s' % (ex))
-                
+
     return docs
 
 
@@ -109,11 +111,10 @@ def split_documents(documents: list[Document]) -> tuple[list[Document], list[Doc
                text_docs.append(doc)
     return text_docs, python_docs
 
-
 @click.command()
 @click.option(
     "--device_type",
-    default="cuda" if torch.cuda.is_available() else "cpu",
+    default=default_device_type(),
     type=click.Choice(
         [
             "cpu",
@@ -137,7 +138,7 @@ def split_documents(documents: list[Document]) -> tuple[list[Document], list[Doc
             "mtia",
         ],
     ),
-    help="Device to run on. (Default is cuda)",
+    help=f"Device to run on. (Default is {default_device_type()})",
 )
 def main(device_type):
     # Load documents and split in chunks
@@ -171,7 +172,7 @@ def main(device_type):
         persist_directory=PERSIST_DIRECTORY,
         client_settings=CHROMA_SETTINGS,
     )
-   
+
 
 
 if __name__ == "__main__":
diff --git a/run_localGPT.py b/run_localGPT.py
index c9f0a2fa..aee6fdce 100644
--- a/run_localGPT.py
+++ b/run_localGPT.py
@@ -3,6 +3,7 @@
 import click
 import torch
 import utils
+from utils import default_device_type
 from langchain.chains import RetrievalQA
 from langchain.embeddings import HuggingFaceInstructEmbeddings
 from langchain.llms import HuggingFacePipeline
@@ -161,11 +162,11 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"):
     return qa
 
 
-# chose device typ to run on as well as to show source documents.
+# chose device type to run on as well as to show source documents.
 @click.command()
 @click.option(
     "--device_type",
-    default="cuda" if torch.cuda.is_available() else "cpu",
+    default=default_device_type(),
     type=click.Choice(
         [
             "cpu",
@@ -189,7 +190,7 @@ def retrieval_qa_pipline(device_type, use_history, promptTemplate_type="llama"):
             "mtia",
         ],
     ),
-    help="Device to run on. (Default is cuda)",
+    help=f"Device to run on. (Default is {default_device_type()})",
 )
 @click.option(
     "--show_sources",
@@ -269,7 +270,7 @@ def main(device_type, show_sources, use_history, model_type, save_qa):
                 print("\n> " + document.metadata["source"] + ":")
                 print(document.page_content)
             print("----------------------------------SOURCE DOCUMENTS---------------------------")
-        
+
         # Log the Q&A to CSV only if save_qa is True
         if save_qa:
             utils.log_to_csv(query, answer)
diff --git a/utils.py b/utils.py
index 0440d214..b5c780ff 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,7 @@
 import os
 import csv
+import torch
+import sys
 from datetime import datetime
 
 def log_to_csv(question, answer):
@@ -22,4 +24,10 @@ def log_to_csv(question, answer):
     with open(log_path, mode='a', newline='', encoding='utf-8') as file:
         writer = csv.writer(file)
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        writer.writerow([timestamp, question, answer])
\ No newline at end of file
+        writer.writerow([timestamp, question, answer])
+
+def has_mps():
+    return sys.platform == "darwin" and torch.backends.mps.is_available()
+
+def default_device_type():
+    return "cuda" if torch.cuda.is_available() else "mps" if has_mps() else "cpu"