diff --git a/modules/models_settings.py b/modules/models_settings.py
index 3a2400d4..47dbc020 100644
--- a/modules/models_settings.py
+++ b/modules/models_settings.py
@@ -2,7 +2,7 @@ import functools
 import json
 import re
 import subprocess
-from math import exp
+from math import floor
 from pathlib import Path
 
 import gradio as gr
@@ -154,10 +154,11 @@ def get_model_metadata(model):
     for pat in settings:
         if re.match(pat.lower(), Path(model).name.lower()):
             for k in settings[pat]:
+                new_k = k
                 if k == 'n_gpu_layers':
-                    k = 'gpu_layers'
+                    new_k = 'gpu_layers'
 
-                model_settings[k] = settings[pat][k]
+                model_settings[new_k] = settings[pat][k]
 
     # Load instruction template if defined by name rather than by value
     if model_settings['instruction_template'] != 'Custom (obtained from model metadata)':
@@ -331,8 +332,6 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
     n_layers = None
     n_kv_heads = None
     embedding_dim = None
-    context_length = None
-    feed_forward_dim = None
 
     for key, value in metadata.items():
         if key.endswith('.block_count'):
@@ -341,10 +340,6 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
             n_kv_heads = value
         elif key.endswith('.embedding_length'):
             embedding_dim = value
-        elif key.endswith('.context_length'):
-            context_length = value
-        elif key.endswith('.feed_forward_length'):
-            feed_forward_dim = value
 
     if gpu_layers > n_layers:
         gpu_layers = n_layers
@@ -359,32 +354,29 @@ def estimate_vram(gguf_file, gpu_layers, ctx_size, cache_type):
 
     # Derived features
     size_per_layer = size_in_mb / max(n_layers, 1e-6)
-    context_per_layer = context_length / max(n_layers, 1e-6)
-    ffn_per_embedding = feed_forward_dim / max(embedding_dim, 1e-6)
     kv_cache_factor = n_kv_heads * cache_type * ctx_size
-
-    # Helper function for smaller
-    def smaller(x, y):
-        return 1 if x < y else 0
+    embedding_per_context = embedding_dim / ctx_size
 
     # Calculate VRAM using the model
     # Details: https://oobabooga.github.io/blog/posts/gguf-vram-formula/
     vram = (
-        (size_per_layer - 21.19195204848197)
-        * exp(0.0001047328491557063 * size_in_mb * smaller(ffn_per_embedding, 2.671096993407845))
-        + 0.0006621544775632052 * context_per_layer
-        + 3.34664386576376e-05 * kv_cache_factor
-    ) * (1.363306170123392 + gpu_layers) + 1255.163594536052
+        (size_per_layer - 17.99552795246051 + 3.148552680382576e-05 * kv_cache_factor)
+        * (gpu_layers + max(0.9690636483914102, cache_type - (floor(50.77817218646521 * embedding_per_context) + 9.987899908205632)))
+        + 1516.522943869404
+    )
 
     return vram
 
 
-def get_nvidia_free_vram():
+def get_nvidia_vram(return_free=True):
     """
-    Calculates the total free VRAM across all NVIDIA GPUs by parsing nvidia-smi output.
+    Calculates VRAM statistics across all NVIDIA GPUs by parsing nvidia-smi output.
+
+    Args:
+        return_free (bool): If True, returns free VRAM. If False, returns total VRAM.
 
     Returns:
-        int: The total free VRAM in MiB summed across all detected NVIDIA GPUs.
+        int: Either the total free VRAM or total VRAM in MiB summed across all detected NVIDIA GPUs.
              Returns -1 if nvidia-smi command fails (not found, error, etc.).
              Returns 0 if nvidia-smi succeeds but no GPU memory info found.
     """
@@ -412,17 +404,21 @@ def get_nvidia_free_vram():
             # No GPUs found in expected format
             return 0
 
+        total_vram_mib = 0
         total_free_vram_mib = 0
+
         for used_mem_str, total_mem_str in matches:
             try:
                 used_mib = int(used_mem_str)
                 total_mib = int(total_mem_str)
+                total_vram_mib += total_mib
                 total_free_vram_mib += (total_mib - used_mib)
             except ValueError:
                 # Skip malformed entries
                 pass
 
-        return total_free_vram_mib
+        # Return either free or total VRAM based on the flag
+        return total_free_vram_mib if return_free else total_vram_mib
 
     except FileNotFoundError:
         # nvidia-smi not found (likely no NVIDIA drivers installed)
@@ -473,10 +469,12 @@ def update_gpu_layers_and_vram(loader, model, gpu_layers, ctx_size, cache_type,
             # No user setting, auto-adjust from the maximum
             current_layers = max_layers  # Start from max
 
-            # Auto-adjust based on available VRAM
-            available_vram = get_nvidia_free_vram()
+            # Auto-adjust based on available/total VRAM
+            # If a model is loaded and it's for the UI, use the total VRAM to avoid confusion
+            return_free = False if (for_ui and shared.model_name not in [None, 'None']) else True
+            available_vram = get_nvidia_vram(return_free=return_free)
             if available_vram > 0:
-                tolerance = 906
+                tolerance = 577
                 while current_layers > 0 and estimate_vram(model, current_layers, ctx_size, cache_type) > available_vram - tolerance:
                     current_layers -= 1