diff --git a/inference/core/env.py b/inference/core/env.py
index ded1c02d7..542a37995 100644
--- a/inference/core/env.py
+++ b/inference/core/env.py
@@ -532,6 +532,9 @@
     warnings.simplefilter("ignore", ModelDependencyMissing)
 
 DISK_CACHE_CLEANUP = str2bool(os.getenv("DISK_CACHE_CLEANUP", "True"))
+MEMORY_FREE_THRESHOLD = float(
+    os.getenv("MEMORY_FREE_THRESHOLD", "0.0")
+)  # percentage of free memory, 0 disables memory pressure detection
 
 # Stream manager configuration
 try:
diff --git a/inference/core/managers/decorators/fixed_size_cache.py b/inference/core/managers/decorators/fixed_size_cache.py
index e2dc4eafc..21b91a48a 100644
--- a/inference/core/managers/decorators/fixed_size_cache.py
+++ b/inference/core/managers/decorators/fixed_size_cache.py
@@ -4,7 +4,7 @@
 from inference.core import logger
 from inference.core.entities.requests.inference import InferenceRequest
 from inference.core.entities.responses.inference import InferenceResponse
-from inference.core.env import DISK_CACHE_CLEANUP
+from inference.core.env import DISK_CACHE_CLEANUP, MEMORY_FREE_THRESHOLD
 from inference.core.managers.base import Model, ModelManager
 from inference.core.managers.decorators.base import ModelManagerDecorator
 from inference.core.managers.entities import ModelDescription
@@ -43,7 +43,9 @@ def add_model(
             return None
 
         logger.debug(f"Current capacity of ModelManager: {len(self)}/{self.max_size}")
-        while len(self) >= self.max_size:
+        while len(self) >= self.max_size or (
+            MEMORY_FREE_THRESHOLD and self.memory_pressure_detected()
+        ):
             to_remove_model_id = self._key_queue.popleft()
             super().remove(
                 to_remove_model_id, delete_from_disk=DISK_CACHE_CLEANUP
@@ -141,3 +143,23 @@ def _resolve_queue_id(
         self, model_id: str, model_id_alias: Optional[str] = None
     ) -> str:
         return model_id if model_id_alias is None else model_id_alias
+
+    def memory_pressure_detected(self) -> bool:
+        return_boolean = False
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                free_memory, total_memory = torch.cuda.mem_get_info()
+                return_boolean = (
+                    float(free_memory / total_memory) < MEMORY_FREE_THRESHOLD
+                )
+                logger.debug(
+                    f"Free memory: {free_memory}, Total memory: {total_memory}, threshold: {MEMORY_FREE_THRESHOLD}, return_boolean: {return_boolean}"
+                )
+            # TODO: Add memory calculation for other non-CUDA devices
+        except Exception as e:
+            logger.error(
+                f"Failed to check CUDA memory pressure: {e}, returning {return_boolean}"
+            )
+        return return_boolean