diff --git a/vllm/executor/cpu_executor.py b/vllm/executor/cpu_executor.py
index eda4e8989c16..33e67d8b3eec 100644
--- a/vllm/executor/cpu_executor.py
+++ b/vllm/executor/cpu_executor.py
@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 import torch
 
@@ -61,7 +61,7 @@ def _init_worker(self):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """
diff --git a/vllm/executor/executor_base.py b/vllm/executor/executor_base.py
index c18edd75d7a4..bbfbfc689c99 100644
--- a/vllm/executor/executor_base.py
+++ b/vllm/executor/executor_base.py
@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -31,7 +31,7 @@ def __init__(
         raise NotImplementedError
 
     @abstractmethod
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available blocks for the GPU KV cache and
         swappable CPU KV cache.
 
@@ -39,7 +39,7 @@ def determine_num_available_blocks(self) -> tuple[int, int]:
         ExecutorBase may require modification of the result, e.g. to ensure the
         selected cache sizes are compatible with all workers.
 
-        Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
+        Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
         are blocks that are "active" on the device and can be appended to.
         num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
         appended to.
diff --git a/vllm/executor/gpu_executor.py b/vllm/executor/gpu_executor.py
index 80ca5cb7367c..f20221a0b941 100644
--- a/vllm/executor/gpu_executor.py
+++ b/vllm/executor/gpu_executor.py
@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -66,7 +66,7 @@ def _init_worker(self):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """
diff --git a/vllm/executor/neuron_executor.py b/vllm/executor/neuron_executor.py
index 57436a85cfa2..ee8e87432fa6 100644
--- a/vllm/executor/neuron_executor.py
+++ b/vllm/executor/neuron_executor.py
@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -47,7 +47,7 @@ def _init_worker(self):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """
diff --git a/vllm/executor/ray_gpu_executor.py b/vllm/executor/ray_gpu_executor.py
index 6c0ccd7e64c9..25f1b024a005 100644
--- a/vllm/executor/ray_gpu_executor.py
+++ b/vllm/executor/ray_gpu_executor.py
@@ -3,7 +3,7 @@
 import os
 import pickle
 from collections import defaultdict
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -197,7 +197,7 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
             max_parallel_loading_workers,
         )
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks.
 
         This invokes `determine_num_available_blocks` on each worker and takes
@@ -205,7 +205,7 @@ def determine_num_available_blocks(self) -> tuple[int, int]:
         compatible with all workers.
 
         Returns:
-            - tuple[num_gpu_blocks, num_cpu_blocks]
+            - Tuple[num_gpu_blocks, num_cpu_blocks]
         """
         # Get the maximum number of blocks that can be allocated on GPU and CPU.
         num_blocks = self._run_workers("determine_num_available_blocks", )
diff --git a/vllm/worker/cpu_worker.py b/vllm/worker/cpu_worker.py
index 3989207e8dd8..41341b063bed 100644
--- a/vllm/worker/cpu_worker.py
+++ b/vllm/worker/cpu_worker.py
@@ -1,5 +1,5 @@
 """A CPU worker class."""
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.distributed
@@ -157,7 +157,7 @@ def init_device(self) -> None:
     def load_model(self):
         self.model_runner.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of blocks available for the KV cache.
 
         This determines how many KV blocks can fit into the configured CPU
diff --git a/vllm/worker/neuron_worker.py b/vllm/worker/neuron_worker.py
index 6136d50d0c06..2f22f82c045d 100644
--- a/vllm/worker/neuron_worker.py
+++ b/vllm/worker/neuron_worker.py
@@ -1,5 +1,5 @@
 """A Neuron worker class."""
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import torch
 import torch.distributed
@@ -40,7 +40,7 @@ def init_device(self) -> None:
     def load_model(self):
         self.model_runner.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks.
 
         Swapping is not yet supported, so always return num_cpu_blocks=0.
diff --git a/vllm/worker/worker_base.py b/vllm/worker/worker_base.py
index e3027c406ffe..d8c9febb1158 100644
--- a/vllm/worker/worker_base.py
+++ b/vllm/worker/worker_base.py
@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 from vllm.lora.request import LoRARequest
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
@@ -18,14 +18,14 @@ def init_device(self) -> None:
         raise NotImplementedError
 
     @abstractmethod
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available blocks for the GPU KV cache and
         swappable CPU KV cache.
 
         The implementation may run profiling or other heuristics to determine
         the size of caches.
 
-        Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
+        Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
         are blocks that are "active" on the device and can be appended to.
         num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
         appended to.