diff --git a/backend/memory_management.py b/backend/memory_management.py
index c3d80edb..66143633 100644
--- a/backend/memory_management.py
+++ b/backend/memory_management.py
@@ -350,7 +350,7 @@ def bake_gguf_model(model):
     return model
 
 
-def module_size(module, exclude_device=None, return_split=False):
+def module_size(module, exclude_device=None, include_device=None, return_split=False):
     module_mem = 0
     weight_mem = 0
     weight_patterns = ['weight']
@@ -362,6 +362,10 @@ def module_size(module, exclude_device=None, return_split=False):
             if t.device == exclude_device:
                 continue
 
+        if include_device is not None:
+            if t.device != include_device:
+                continue
+
         element_size = t.element_size()
 
         if getattr(p, 'quant_type', None) in ['fp4', 'nf4']:
@@ -439,12 +443,13 @@ class LoadedModel:
         self.memory_required = memory_required
         self.model_accelerated = False
         self.device = model.load_device
+        self.inclusive_memory = 0
+        self.exclusive_memory = 0
 
-    def model_memory(self):
-        return self.model.model_size()
-
-    def model_memory_required(self, device=None):
-        return module_size(self.model.model, exclude_device=device)
+    def compute_inclusive_exclusive_memory(self):
+        self.inclusive_memory = module_size(self.model.model, include_device=self.device)
+        self.exclusive_memory = module_size(self.model.model, exclude_device=self.device)
+        return
 
     def model_load(self, model_gpu_memory_when_using_cpu_swap=-1):
         patch_model_to = None
@@ -548,9 +553,6 @@ def unload_model_clones(model):
         if model.is_clone(current_loaded_models[i].model):
             to_unload = [i] + to_unload
 
-    if len(to_unload) > 0:
-        print(f"Reuse {len(to_unload)} loaded models")
-
     for i in to_unload:
         current_loaded_models.pop(i).model_unload(avoid_model_moving=True)
 
@@ -632,10 +634,13 @@ def load_models_gpu(models, memory_required=0):
 
         return
 
-    total_memory_required = {}
     for loaded_model in models_to_load:
         unload_model_clones(loaded_model.model)
-        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required()
+
+    total_memory_required = {}
+    for loaded_model in models_to_load:
+        loaded_model.compute_inclusive_exclusive_memory()
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.exclusive_memory + loaded_model.inclusive_memory * 0.25
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
@@ -652,16 +657,20 @@ def load_models_gpu(models, memory_required=0):
         model_gpu_memory_when_using_cpu_swap = -1
 
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
-            model_memory = loaded_model.model_memory_required(torch_dev)
+            model_require = loaded_model.exclusive_memory
+            previously_loaded = loaded_model.inclusive_memory
+
             current_free_mem = get_free_memory(torch_dev)
             inference_memory = minimum_inference_memory()
-            estimated_remaining_memory = current_free_mem - model_memory - inference_memory
+            estimated_remaining_memory = current_free_mem - model_require - inference_memory
 
-            print(f"[Memory Management] Target: {loaded_model.model.model.__class__.__name__}, Free GPU: {current_free_mem / (1024 * 1024):.2f} MB, Model Require: {model_memory / (1024 * 1024):.2f} MB, Inference Require: {inference_memory / (1024 * 1024):.2f} MB, Remaining: {estimated_remaining_memory / (1024 * 1024):.2f} MB, ", end="")
+            print(f"[Memory Management] Target: {loaded_model.model.model.__class__.__name__}, Free GPU: {current_free_mem / (1024 * 1024):.2f} MB, Model Require: {model_require / (1024 * 1024):.2f} MB, Previously Loaded: {previously_loaded / (1024 * 1024):.2f} MB, Inference Require: {inference_memory / (1024 * 1024):.2f} MB, Remaining: {estimated_remaining_memory / (1024 * 1024):.2f} MB, ", end="")
 
             if estimated_remaining_memory < 0:
                 vram_set_state = VRAMState.LOW_VRAM
                 model_gpu_memory_when_using_cpu_swap = compute_model_gpu_memory_when_using_cpu_swap(current_free_mem, inference_memory)
+                if previously_loaded > 0:
+                    model_gpu_memory_when_using_cpu_swap = previously_loaded
 
         if vram_set_state == VRAMState.NO_VRAM:
             model_gpu_memory_when_using_cpu_swap = 0