diff --git a/modules/llama_cpp_server.py b/modules/llama_cpp_server.py
index ecc543f3..7199470d 100644
--- a/modules/llama_cpp_server.py
+++ b/modules/llama_cpp_server.py
@@ -301,6 +301,20 @@ class LlamaServer:
                 cmd += ["--device-draft", shared.args.device_draft]
             if shared.args.ctx_size_draft > 0:
                 cmd += ["--ctx-size-draft", str(shared.args.ctx_size_draft)]
+        if shared.args.extra_flags:
+            # Clean up the input
+            extra_flags = shared.args.extra_flags.strip()
+            if extra_flags.startswith('"') and extra_flags.endswith('"'):
+                extra_flags = extra_flags[1:-1].strip()
+            elif extra_flags.startswith("'") and extra_flags.endswith("'"):
+                extra_flags = extra_flags[1:-1].strip()
+
+            for flag_item in extra_flags.split(';'):
+                if '=' in flag_item:
+                    flag, value = flag_item.split('=', 1)
+                    cmd += [f"--{flag}", value]
+                else:
+                    cmd.append(f"--{flag_item}")
 
         env = os.environ.copy()
         if os.name == 'posix':
diff --git a/modules/loaders.py b/modules/loaders.py
index d256e1e7..9442a147 100644
--- a/modules/loaders.py
+++ b/modules/loaders.py
@@ -12,6 +12,7 @@ loaders_and_params = OrderedDict({
         'n_ctx',
         'cache_type',
         'tensor_split',
+        'extra_flags',
         'rope_freq_base',
         'compress_pos_emb',
         'flash_attn',
diff --git a/modules/shared.py b/modules/shared.py
index 98ec50b2..b4f87082 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -128,6 +128,7 @@ group.add_argument('--tensor-split', type=str, default=None, help='Split the mod
 group.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp.')
 group.add_argument('--no-kv-offload', action='store_true', help='Do not offload the  K, Q, V to the GPU. This saves VRAM but reduces the performance.')
 group.add_argument('--row-split', action='store_true', help='Split the model by rows across GPUs. This may improve multi-gpu performance.')
+group.add_argument('--extra-flags', type=str, default=None, help='Extra flags to pass to llama-server. Format: "flag1=value1;flag2;flag3=value3". Example: "override-tensor=exps=CPU"')
 
 # Speculative decoding
 group = parser.add_argument_group('Speculative decoding')
diff --git a/modules/ui.py b/modules/ui.py
index 6fc5e955..19b76cee 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -114,6 +114,7 @@ def list_model_elements():
         'max_seq_len',
         'cache_type',
         'tensor_split',
+        'extra_flags',
         'gpu_split',
         'alpha_value',
         'rope_freq_base',
diff --git a/modules/ui_model_menu.py b/modules/ui_model_menu.py
index 56b6903f..f3319cfb 100644
--- a/modules/ui_model_menu.py
+++ b/modules/ui_model_menu.py
@@ -56,6 +56,7 @@ def create_ui():
                             shared.gradio['cache_type'] = gr.Dropdown(label="cache_type", choices=['fp16', 'q8_0', 'q4_0', 'fp8', 'q8', 'q6', 'q4'], value=shared.args.cache_type, info='Valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV2 - fp16, fp8, q8, q6, q4.')
                             shared.gradio['tensor_split'] = gr.Textbox(label='tensor_split', info='List of proportions to split the model across multiple GPUs. Example: 60,40')
                             shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
+                            shared.gradio['extra_flags'] = gr.Textbox(label='extra-flags', info='Additional flags to pass to llama-server. Format: "flag1=value1;flag2;flag3=value3". Example: "override-tensor=exps=CPU"')
                             shared.gradio['cpu_memory'] = gr.Number(label="Maximum CPU memory in GiB. Use this for CPU offloading.", value=shared.args.cpu_memory)
                             shared.gradio['alpha_value'] = gr.Number(label='alpha_value', value=shared.args.alpha_value, precision=2, info='Positional embeddings alpha factor for NTK RoPE scaling. Recommended values (NTKv1): 1.75 for 1.5x context, 2.5 for 2x context. Use either this or compress_pos_emb, not both.')
                             shared.gradio['rope_freq_base'] = gr.Number(label='rope_freq_base', value=shared.args.rope_freq_base, precision=0, info='Positional embeddings frequency base for NTK RoPE scaling. Related to alpha_value by rope_freq_base = 10000 * alpha_value ^ (64 / 63). 0 = from model.')