diff --git a/backends/exllamav2/model.py b/backends/exllamav2/model.py
index ff11531..d692233 100644
--- a/backends/exllamav2/model.py
+++ b/backends/exllamav2/model.py
@@ -90,6 +90,7 @@ class ExllamaV2Container:
 
     # GPU split vars
     gpu_split: Optional[list] = None
+    draft_gpu_split: Optional[list] = None
     gpu_split_auto: bool = True
     autosplit_reserve: List[float] = [96 * 1024**2]
     use_tp: bool = False
@@ -180,6 +181,7 @@ async def create(cls, model_directory: pathlib.Path, quiet=False, **kwargs):
             )
             draft_model_path = draft_model_path / draft_model_name
 
+            self.draft_gpu_split = draft_args.get("draft_gpu_split")
             self.draft_model_dir = draft_model_path
             self.draft_config.model_dir = str(draft_model_path.resolve())
             self.draft_config.prepare()
@@ -232,6 +234,16 @@ async def create(cls, model_directory: pathlib.Path, quiet=False, **kwargs):
                     for value in autosplit_reserve_megabytes
                 ]
 
+            if self.draft_gpu_split:
+                self.gpu_split_auto = False
+                self.gpu_split = gpu_split
+
+                gpu_device_list = [
+                    device_idx
+                    for device_idx, memory in enumerate(self.draft_gpu_split)
+                    if memory > 0
+                ]
+
         # Hardcode max output length to 16
         self.config.max_output_len = 16
 
@@ -617,21 +629,37 @@ def progress(loaded_modules: int, total_modules: int)
 
             # Draft uses the autosplit loader, so create a cache that reflects this
             draft_cache_class = self.get_cache_class(self.draft_cache_mode)
-            self.draft_cache = self.create_cache(
-                cache_class=draft_cache_class,
-                autosplit=True,
-                use_tp=False,
-                model=self.draft_model,
-            )
 
-            for value in self.draft_model.load_autosplit_gen(
-                self.draft_cache,
-                reserve_vram=autosplit_reserve,
-                last_id_only=True,
-                callback_gen=progress_callback,
-            ):
-                if value:
-                    yield value
+            if self.draft_gpu_split:
+                for value in self.draft_model.load_gen(
+                    self.draft_gpu_split,
+                    callback_gen=progress_callback,
+                ):
+                    if value:
+                        yield value
+
+                self.draft_cache = self.create_cache(
+                    cache_class=draft_cache_class,
+                    autosplit=False,
+                    use_tp=False,
+                    model=self.draft_model,
+                )
+            else:
+                self.draft_cache = self.create_cache(
+                    cache_class=draft_cache_class,
+                    autosplit=True,
+                    use_tp=False,
+                    model=self.draft_model,
+                )
+
+                for value in self.draft_model.load_autosplit_gen(
+                    self.draft_cache,
+                    reserve_vram=autosplit_reserve,
+                    last_id_only=True,
+                    callback_gen=progress_callback,
+                ):
+                    if value:
+                        yield value
 
             # Test VRAM allocation with a full-length forward pass
             input_ids = torch.zeros((1, self.config.max_input_len), dtype=torch.long)