diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index bdf0d0f72289e..4ecf72b56ef66 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -241,10 +241,11 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             device=self.device)
 
         # OPTIMIZATION: Cache the tensors rather than creating them every step.
+        # Keep in int64 to avoid overflow with long context
         self.arange_np = np.arange(max(self.max_num_reqs + 1,
                                        self.max_model_len,
                                        self.max_num_tokens),
-                                   dtype=np.int32)
+                                   dtype=np.int64)
         # NOTE(woosuk): These tensors are "stateless", i.e., they are literally
         # a faster version of creating a new tensor every time. Thus, we should
         # not make any assumptions about the values in these tensors.
diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py
index 5d94f675f92e8..e9cb0dbe8b5e9 100644
--- a/vllm/v1/worker/tpu_model_runner.py
+++ b/vllm/v1/worker/tpu_model_runner.py
@@ -219,7 +219,8 @@ class TPUModelRunner:
 
         # Range tensor with values [0 .. self.max_num_tokens - 1].
         # Used to initialize positions / context_lens / seq_lens
-        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int32)
+        # Keep in int64 to avoid overflow with long context
+        self.arange_np = np.arange(self.max_num_tokens, dtype=np.int64)
         self.num_reqs_paddings = _get_req_paddings(
             min_req_size=MIN_NUM_SEQS, max_req_size=self.max_num_reqs)