diff --git a/examples/offline_inference/disaggregated_prefill.py b/examples/offline_inference/disaggregated_prefill.py
new file mode 100644
index 000000000000..2e41cabaccaf
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+++ b/examples/offline_inference/disaggregated_prefill.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+This file demonstrates the example usage of disaggregated prefilling
+We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
+and then transfer the KV cache between them.
+"""
+import os
+import time
+from multiprocessing import Event, Process
+
+from vllm import LLM, SamplingParams
+from vllm.config import KVTransferConfig
+
+
+def run_prefill(prefill_done):
+    # We use GPU 0 for prefill node.
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+    # The prefill node receives two requests, while the decode node receives
+    # three requests. So the decode node will only receive the KV Cache for
+    # requests 1 and 3. The decode node will use the KV Cache of requests 1
+    # and 3 and do prefilling on request 2.
+    prompts = [
+        "Hello, my name is",
+        # "Hi, your name is",
+        # The decode node will actually "prefill" this request.
+        "Tell me a very long story",
+    ]
+    sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1)
+
+    # Using PyNcclConnector to transmit KV caches between vLLM instances.
+    # This instance is the prefill node (kv_producer, rank 0).
+    # The number of parallel instances for KV cache transfer is set to 2,
+    # as required for PyNcclConnector.
+    ktc = KVTransferConfig.from_cli(
+        '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2}'
+    )
+
+    # Set GPU memory utilization to 0.8 for an A6000 GPU with 40GB
+    # memory. You may need to adjust the value to fit your GPU.
+    llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+              kv_transfer_config=ktc,
+              max_model_len=2000,
+              gpu_memory_utilization=0.8)
+
+    llm.generate(prompts, sampling_params)
+    print("Prefill node is finished.")
+    prefill_done.set()
+
+    # To keep the prefill node running in case the decode node is not done;
+    # otherwise, the script might exit prematurely, causing incomplete decoding.
+    try:
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        print("Script stopped by user.")
+
+
+def run_decode(prefill_done):
+    # We use GPU 1 for decode node.
+    os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
+    prompts = [
+        "Hello, my name is",
+        "Hi, your name is",
+        "Tell me a very long story",
+    ]
+    sampling_params = SamplingParams(temperature=0, top_p=0.95)
+
+    # Using PyNcclConnector to transmit KV caches between vLLM instances.
+    # This instance is the decode node (kv_consumer, rank 1).
+    # The number of parallel instances for KV cache transfer is set to 2,
+    # as required for PyNcclConnector.
+    ktc = KVTransferConfig.from_cli(
+        '{"kv_connector":"PyNcclConnector","kv_role":"kv_consumer","kv_rank":1,"kv_parallel_size":2}'
+    )
+
+    # Set GPU memory utilization to 0.8 for an A6000 GPU with 40GB
+    # memory. You may need to adjust the value to fit your GPU.
+    llm = LLM(model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+              kv_transfer_config=ktc,
+              max_model_len=2000,
+              gpu_memory_utilization=0.8)
+
+    # Wait for the producer to start the pipe
+    print("Waiting for prefill node to finish...")
+    prefill_done.wait()
+
+    # At this point when the prefill_done is set, the kv-cache should have been
+    # transferred to this decode node, so we can start decoding.
+    outputs = llm.generate(prompts, sampling_params)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+if __name__ == "__main__":
+    prefill_done = Event()
+    prefill_process = Process(target=run_prefill, args=(prefill_done, ))
+    decode_process = Process(target=run_decode, args=(prefill_done, ))
+
+    # Start prefill node
+    prefill_process.start()
+
+    # Start decode node
+    decode_process.start()
+
+    # Terminate the prefill node when decode is finished
+    decode_process.join()
+    prefill_process.terminate()