diff --git a/unsloth/kernels/utils.py b/unsloth/kernels/utils.py
index 5c955a3c8d..1c65246b31 100644
--- a/unsloth/kernels/utils.py
+++ b/unsloth/kernels/utils.py
@@ -78,7 +78,7 @@ def calculate_settings(n : int) -> (int, int,):
 # INTEL GPU specific logic
 if DEVICE_TYPE == "xpu":
     # TODO: Changed here after adding XPU BNB support
-    HAS_XPU_STREAM = False
+    HAS_XPU_STREAM = True
     def get_ptr(x: Optional[torch.Tensor]):
         raise RuntimeError("XPU BNB support is not implemented yet. This function should not be called.")
 else:
diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py
index 602f7ee90f..1af631f965 100644
--- a/unsloth/models/_utils.py
+++ b/unsloth/models/_utils.py
@@ -142,6 +142,12 @@
 import logging
 logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.CRITICAL+1)
 
+def get_device_num():
+    if DEVICE_TYPE == "xpu":
+        return torch.xpu.device_count()
+    else:
+        return torch.cuda.device_count()
+
 # Ignore logging messages
 class HideLoggingMessage(logging.Filter):
     __slots__ = "text",
@@ -738,7 +744,7 @@ def get_statistics():
         pass
     pass
     try:
-        devices = torch.cuda.device_count()
+        devices = get_device_num()
         _get_statistics(f"{devices if devices <= 8 else 9}")
     except:
         pass
@@ -765,7 +771,7 @@ def get_statistics():
 )
 exec(BitsAndBytesConfig__init__, globals())
 
-if torch.cuda.device_count() == 1:
+if get_device_num() == 1:
     from accelerate.utils.dataclasses import DistributedType
     def _prepare_backend(self, *args, **kwargs): return None, DistributedType.NO
     import accelerate.state
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 7d56dac2ec..f08b4762eb 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -85,6 +85,11 @@
 HAS_XFORMERS = xformers is not None
 BlockDiagonalCausalMask = xformers.attn_bias.BlockDiagonalCausalMask if HAS_XFORMERS else None
 
+def clean_gpu_cache():
+    if DEVICE_TYPE == "xpu":
+        torch.xpu.empty_cache()
+    else:
+        torch.cuda.empty_cache()
 
 def original_apply_qkv(self, X):
     Q = self.q_proj(X)
@@ -1752,10 +1757,11 @@ def from_pretrained(
             if not is_vLLM_available():
                 print("Unsloth: vLLM is not installed! Will use Unsloth inference!")
                 fast_inference = False
-            major_version, minor_version = torch.cuda.get_device_capability()
-            if major_version < 7:
-                print("Unsloth: vLLM does not work on older GPUs - will switch to Unsloth inference!")
-                fast_inference = False
+            if DEVICE_TYPE == "cuda":
+                major_version, minor_version = torch.cuda.get_device_capability()
+                if major_version < 7:
+                    print("Unsloth: vLLM does not work on older GPUs - will switch to Unsloth inference!")
+                    fast_inference = False
             if unsloth_vllm_standby and os.environ.get("UNSLOTH_VLLM_STANDBY", "0") == "0":
                 raise RuntimeError("Unsloth: `unsloth_vllm_standby` is True, but  environment variable `UNSLOTH_VLLM_STANDBY` is not set to 1!")
         pass
@@ -1779,8 +1785,8 @@ def from_pretrained(
             num_gpus = torch.xpu.device_count()
             gpu_stats_snippet = f"Intel Toolkit: {gpu_version}."
 
-            # TODO: After adding vLLM support for XPU, changed this
-            vllm_version = ""
+            try:    vllm_version = f" vLLM: {importlib_version('vllm')}."
+            except: vllm_version = ""
         else:
             raise ValueError(f"Unsloth: Unsupported device type: {DEVICE_TYPE}")
 
@@ -2020,7 +2026,10 @@ def from_pretrained(
         import gc
         for _ in range(3):
             gc.collect()
-            torch.cuda.empty_cache()"""
+            if DEVICE_TYPE == "xpu":
+                torch.xpu.empty_cache()
+            else:
+                torch.cuda.empty_cache()"""
 
         debug_info = debug_info.split('\n')
         debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
@@ -2508,7 +2517,7 @@ def get_peft_model(
             # Remove old items to save VRAM
             for _ in range(3):
                 gc.collect()
-                torch.cuda.empty_cache()
+                clean_gpu_cache()
             pass
 
             if train_lm_head:
@@ -2519,7 +2528,7 @@ def get_peft_model(
             # Remove old items to save VRAM
             for _ in range(3):
                 gc.collect()
-                torch.cuda.empty_cache()
+                clean_gpu_cache()
             pass
         pass
 
@@ -2580,7 +2589,7 @@ def get_peft_model(
         # Clear deleted GPU items
         for _ in range(3):
             gc.collect()
-            torch.cuda.empty_cache()
+            clean_gpu_cache()
         pass
 
         # Patch for fast inference
@@ -2796,7 +2805,7 @@ def patch_peft_model(
         # Clear deleted GPU items
         for _ in range(3):
             gc.collect()
-            torch.cuda.empty_cache()
+            clean_gpu_cache()
         pass
 
         # Patch for fast inference
diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py
index 9b0f4e4aef..bd5c58384e 100644
--- a/unsloth/models/rl_replacements.py
+++ b/unsloth/models/rl_replacements.py
@@ -26,6 +26,8 @@
 import inspect
 from collections import defaultdict
 from unsloth_zoo.rl_replacements import RL_REPLACEMENTS
+from unsloth import DEVICE_TYPE
+
 RL_EXTRA_ARGS      = defaultdict(list)
 RL_FUNCTIONS       = defaultdict(list)
 RL_PRE_ITEMS       = defaultdict(list)
@@ -258,7 +260,7 @@ def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep)
             if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': self._autocast_dtype = torch.float16
 
         os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
-        with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
+        with torch.amp.autocast(device_type = DEVICE_TYPE, dtype = self._autocast_dtype):
             # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
             logits = model(
                 input_ids = input_ids,