diff --git a/backends/exllamav2/model.py b/backends/exllamav2/model.py
index 43b86334..f656f7f2 100644
--- a/backends/exllamav2/model.py
+++ b/backends/exllamav2/model.py
@@ -27,6 +27,14 @@
 from common.utils import coalesce, unwrap
 from common.logger import init_logger
 
+# Optional imports for dependencies
+try:
+    from exllamav2 import ExLlamaV2Cache_Q4
+
+    _exllamav2_has_int4 = True
+except ImportError:
+    _exllamav2_has_int4 = False
+
 logger = init_logger(__name__)
 
 
@@ -46,7 +54,7 @@ class ExllamaV2Container:
     active_loras: List[ExLlamaV2Lora] = []
 
     # Internal config vars
-    cache_fp8: bool = False
+    cache_mode: str = "FP16"
     use_cfg: bool = False
 
     # GPU split vars
@@ -109,7 +117,15 @@ def progress(loaded_modules: int, total_modules: int,
 
         self.quiet = quiet
 
-        self.cache_fp8 = "cache_mode" in kwargs and kwargs["cache_mode"] == "FP8"
+        cache_mode = unwrap(kwargs.get("cache_mode"), "FP16")
+        if cache_mode == "Q4" and not _exllamav2_has_int4:
+            logger.warning(
+                "Q4 cache is not available "
+                "in the currently installed ExllamaV2 version. Using FP16."
+            )
+            cache_mode = "FP16"
+
+        self.cache_mode = cache_mode
 
         # Turn off GPU split if the user is using 1 GPU
         gpu_count = torch.cuda.device_count()
@@ -398,7 +414,12 @@ def progress(loaded_modules: int, total_modules: int)
                     yield value
 
         batch_size = 2 if self.use_cfg else 1
-        if self.cache_fp8:
+
+        if self.cache_mode == "Q4" and _exllamav2_has_int4:
+            self.cache = ExLlamaV2Cache_Q4(
+                self.model, lazy=self.gpu_split_auto, batch_size=batch_size
+            )
+        elif self.cache_mode == "FP8":
             self.cache = ExLlamaV2Cache_8bit(
                 self.model, lazy=self.gpu_split_auto, batch_size=batch_size
             )
diff --git a/main.py b/main.py
index 38480a59..c866c82f 100644
--- a/main.py
+++ b/main.py
@@ -149,7 +149,7 @@ async def get_current_model():
             rope_scale=MODEL_CONTAINER.config.scale_pos_emb,
             rope_alpha=MODEL_CONTAINER.config.scale_alpha_value,
             max_seq_len=MODEL_CONTAINER.config.max_seq_len,
-            cache_mode="FP8" if MODEL_CONTAINER.cache_fp8 else "FP16",
+            cache_mode=MODEL_CONTAINER.cache_mode,
             prompt_template=prompt_template.name if prompt_template else None,
             num_experts_per_token=MODEL_CONTAINER.config.num_experts_per_token,
             use_cfg=MODEL_CONTAINER.use_cfg,