diff --git a/vllm/v1/worker/gpu/async_utils.py b/vllm/v1/worker/gpu/async_utils.py
new file mode 100644
index 0000000000000..ed11701739986
--- /dev/null
+++ b/vllm/v1/worker/gpu/async_utils.py
@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.v1.outputs import (AsyncModelRunnerOutput, LogprobsTensors,
+                             ModelRunnerOutput, SamplerOutput)
+
+
+class AsyncOutput(AsyncModelRunnerOutput):
+
+    def __init__(
+        self,
+        model_runner_output: ModelRunnerOutput,
+        sampler_output: SamplerOutput,
+        copy_stream: torch.cuda.Stream,
+    ):
+        self.model_runner_output = model_runner_output
+        self.sampler_output = sampler_output
+        self.copy_stream = copy_stream
+        self.copy_event = torch.cuda.Event()
+
+        default_stream = torch.cuda.current_stream()
+        with torch.cuda.stream(self.copy_stream):
+            self.copy_stream.wait_stream(default_stream)
+
+            self.sampled_token_ids = sampler_output.sampled_token_ids.to(
+                "cpu", non_blocking=True)
+            x = sampler_output.logprobs_tensors
+            if x is not None:
+                self.logprobs_tensors = LogprobsTensors(
+                    logprob_token_ids=x.logprob_token_ids.to(
+                        "cpu", non_blocking=True),
+                    logprobs=x.logprobs.to("cpu", non_blocking=True),
+                    selected_token_ranks=x.selected_token_ranks.to(
+                        "cpu", non_blocking=True),
+                )
+            else:
+                self.logprobs_tensors = None
+            self.copy_event.record()
+
+    def get_output(self) -> ModelRunnerOutput:
+        self.copy_event.synchronize()
+        self.model_runner_output.sampled_token_ids = (
+            self.sampled_token_ids.numpy())
+        if self.logprobs_tensors is not None:
+            self.model_runner_output.logprobs = (
+                self.logprobs_tensors.tolists())
+        return self.model_runner_output
diff --git a/vllm/v1/worker/gpu/model_runner.py b/vllm/v1/worker/gpu/model_runner.py
index 153f4af34eb8d..53072ac9d5ecc 100644
--- a/vllm/v1/worker/gpu/model_runner.py
+++ b/vllm/v1/worker/gpu/model_runner.py
@@ -20,6 +20,7 @@ from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput
 from vllm.v1.sample.sampler import SamplerOutput
+from vllm.v1.worker.gpu.async_utils import AsyncOutput
 from vllm.v1.worker.gpu.attn_utils import (get_kv_cache_spec,
                                            init_attn_backend, init_kv_cache)
 from vllm.v1.worker.gpu.block_table import BlockTables
@@ -65,6 +66,10 @@ class GPUModelRunner:
         self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
         self.max_num_reqs = self.scheduler_config.max_num_seqs
 
+        self.use_async_scheduling = self.scheduler_config.async_scheduling
+        assert self.use_async_scheduling
+        self.output_copy_stream = torch.cuda.Stream()
+
         self.req_states = RequestState(
             max_num_reqs=self.max_num_reqs,
             max_model_len=self.max_model_len,
@@ -412,29 +417,32 @@ class GPUModelRunner:
         self,
         sampler_output: SamplerOutput,
         input_batch: InputBatch,
-    ) -> tuple[np.ndarray, np.ndarray]:
+    ) -> AsyncOutput:
         # Get the number of sampled tokens.
         # 0 if chunked-prefilling, 1 if not.
         is_chunked_prefilling = input_batch.is_chunked_prefilling
         num_sampled_tokens = (~is_chunked_prefilling).astype(np.int32)
 
-        # Update the token IDs and the number of tokens.
-        sampled_token_ids_cpu = sampler_output.sampled_token_ids.cpu()
-        sampled_token_ids_np = sampled_token_ids_cpu.numpy()
-        self.req_states.append_token_ids(
-            input_batch.idx_mapping_np,
-            sampled_token_ids_np,
-            num_sampled_tokens,
+        model_runner_output = ModelRunnerOutput(
+            req_ids=input_batch.req_ids,
+            sampled_token_ids=None,
+            num_sampled_tokens=num_sampled_tokens,
+            logprobs=None,
+            prompt_logprobs_dict={},
+            pooler_output=[],
+            kv_connector_output=None,
+            num_nans_in_logits=None,
+        )
+        return AsyncOutput(
+            model_runner_output=model_runner_output,
+            sampler_output=sampler_output,
+            copy_stream=self.output_copy_stream,
         )
-
-        # self.req_states.last_token_ids[input_batch.idx_mapping] = (
-        #     sampler_output.sampled_token_ids)
-        return sampled_token_ids_np, num_sampled_tokens
 
     def execute_model(
         self,
         scheduler_output: SchedulerOutput,
-    ):
+    ) -> AsyncOutput:
         self.update_states(scheduler_output)
         if scheduler_output.total_num_scheduled_tokens == 0:
             return EMPTY_MODEL_RUNNER_OUTPUT
@@ -453,19 +461,4 @@ class GPUModelRunner:
             )
 
         sampler_output = self.sample(hidden_states, input_batch)
-
-        sampled_token_ids_np, num_sampled_tokens = self.postprocess(
-            sampler_output, input_batch)
-        logprobs = None
-        if sampler_output.logprobs_tensors is not None:
-            logprobs = sampler_output.logprobs_tensors.tolists()
-        return ModelRunnerOutput(
-            req_ids=input_batch.req_ids,
-            sampled_token_ids=sampled_token_ids_np,
-            num_sampled_tokens=num_sampled_tokens,
-            logprobs=logprobs,
-            prompt_logprobs_dict={},
-            pooler_output=[],
-            kv_connector_output=None,
-            num_nans_in_logits=None,
-        )
+        return self.postprocess(sampler_output, input_batch)