diff --git a/vae/README.md b/vae/README.md
index cda6a33672..e2a432fd1e 100644
--- a/vae/README.md
+++ b/vae/README.md
@@ -14,8 +14,9 @@ The main.py script accepts the following arguments:
 optional arguments:
   --batch-size		input batch size for training (default: 128)
   --epochs		number of epochs to train (default: 10)
-  --no-cuda		enables CUDA training
-  --mps         enables GPU on macOS
+  --no-cuda		disables CUDA training
+  --no-mps	        disables GPU on macOS
+  --no-xpu		disables XPU training in Intel GPUs
   --seed		random seed (default: 1)
   --log-interval	how many batches to wait before logging training status
-```
\ No newline at end of file
+```
diff --git a/vae/main.py b/vae/main.py
index d69833fbe0..f7915b9ced 100644
--- a/vae/main.py
+++ b/vae/main.py
@@ -13,10 +13,12 @@
                     help='input batch size for training (default: 128)')
 parser.add_argument('--epochs', type=int, default=10, metavar='N',
                     help='number of epochs to train (default: 10)')
-parser.add_argument('--no-cuda', action='store_true', default=False,
+parser.add_argument('--no-cuda', action='store_true',
                     help='disables CUDA training')
-parser.add_argument('--no-mps', action='store_true', default=False,
+parser.add_argument('--no-mps', action='store_true',
                         help='disables macOS GPU training')
+parser.add_argument('--no-xpu', action='store_true',
+                        help='disables Intel XPU training')
 parser.add_argument('--seed', type=int, default=1, metavar='S',
                     help='random seed (default: 1)')
 parser.add_argument('--log-interval', type=int, default=10, metavar='N',
@@ -24,6 +26,7 @@
 args = parser.parse_args()
 args.cuda = not args.no_cuda and torch.cuda.is_available()
 use_mps = not args.no_mps and torch.backends.mps.is_available()
+use_xpu = not args.no_xpu and torch.xpu.is_available()
 
 torch.manual_seed(args.seed)
 
@@ -31,9 +34,13 @@
     device = torch.device("cuda")
 elif use_mps:
     device = torch.device("mps")
+elif use_xpu:
+    device = torch.device("xpu")
 else:
     device = torch.device("cpu")
 
+print('Device to use: ', device)
+
 kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
 train_loader = torch.utils.data.DataLoader(
     datasets.MNIST('../data', train=True, download=True,