diff --git a/examples/llm-api/llm_kv_cache_connector.py b/examples/llm-api/llm_kv_cache_connector.py
index 599fab6f9ac..1eac9a9cd98 100644
--- a/examples/llm-api/llm_kv_cache_connector.py
+++ b/examples/llm-api/llm_kv_cache_connector.py
@@ -15,7 +15,7 @@
 from tensorrt_llm._torch.pyexecutor.kv_cache_connector import (
     KvCacheConnectorScheduler, KvCacheConnectorWorker, SchedulerOutput)
 from tensorrt_llm.bindings.internal.batch_manager import LlmRequest
-from tensorrt_llm.llmapi.llm_args import KvCacheConnectorConfig
+from tensorrt_llm.llmapi.llm_args import KvCacheConnectorConfig, TorchLlmArgs
 
 # This is a simple example of the use of the KV cache connector.
 # It persists KV cache contents into a folder, and can load them back on subsequent runs.
@@ -33,8 +33,8 @@ class PersistentKvCacheConnectorMetadata:
 
 class PersistentKvCacheConnectorWorker(KvCacheConnectorWorker):
 
-    def __init__(self):
-        super().__init__()
+    def __init__(self, llm_args: TorchLlmArgs):
+        super().__init__(llm_args)
 
         self.kv_cache_tensor = None
 
@@ -80,10 +80,10 @@ def get_finished(
 
 class PersistentKvCacheConnectorLeader(KvCacheConnectorScheduler):
 
-    def __init__(self, tokens_per_block):
-        super().__init__()
+    def __init__(self, llm_args: TorchLlmArgs):
+        super().__init__(llm_args)
 
-        self.block_size = tokens_per_block
+        self.block_size = self._llm_args.kv_cache_config.tokens_per_block
         self.pending_loads = {}
 
         self.cache_folder = os.environ.get(CONNECTOR_CACHE_FOLDER_KEY,
diff --git a/tensorrt_llm/_torch/pyexecutor/kv_cache_connector.py b/tensorrt_llm/_torch/pyexecutor/kv_cache_connector.py
index 9bec793a8c4..813b36112fa 100644
--- a/tensorrt_llm/_torch/pyexecutor/kv_cache_connector.py
+++ b/tensorrt_llm/_torch/pyexecutor/kv_cache_connector.py
@@ -47,6 +47,7 @@
 from tensorrt_llm.bindings.internal.batch_manager import \
     KvCacheConnectorManager as KvCacheConnectorManagerCpp
 from tensorrt_llm.bindings.internal.batch_manager import LlmRequest
+from tensorrt_llm.llmapi.llm_args import TorchLlmArgs
 
 from .scheduler import ScheduledRequests
 
@@ -80,7 +81,8 @@ class SchedulerOutput:
 
 class KvCacheConnectorWorker(ABC):
 
-    def __init__(self):
+    def __init__(self, llm_args: TorchLlmArgs):
+        self._llm_args = llm_args
         self._metadata = None
         super().__init__()
 
@@ -160,7 +162,8 @@ def get_finished(
 
 class KvCacheConnectorScheduler(ABC):
 
-    def __init__(self):
+    def __init__(self, llm_args: TorchLlmArgs):
+        self._llm_args = llm_args
         super().__init__()
 
     @abstractmethod
diff --git a/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py b/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
index 5a7502f844f..2ec87a1c1c5 100644
--- a/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
+++ b/tensorrt_llm/_torch/pyexecutor/py_executor_creator.py
@@ -445,11 +445,11 @@ def drafting_loop_wrapper(model):
             # In this case, the worker may be dependent on the scheduler, or vice-versa.
             # To deal with cases like this, we instantiate them both concurrently.
             with ThreadPoolExecutor(max_workers=2) as executor:
-                connector_worker_task = executor.submit(worker_cls)
+                connector_worker_task = executor.submit(worker_cls, llm_args)
 
                 if scheduler_cls is not None and rank == 0:
                     connector_scheduler_task = executor.submit(
-                        scheduler_cls, executor_config.tokens_per_block)
+                        scheduler_cls, llm_args)
                     connector_scheduler = connector_scheduler_task.result()
                 else:
                     connector_scheduler = None