diff --git a/docs/source/models/spec_decode.rst b/docs/source/models/spec_decode.rst
index be901fa881b1..d3c196faff25 100644
--- a/docs/source/models/spec_decode.rst
+++ b/docs/source/models/spec_decode.rst
@@ -14,17 +14,17 @@ Speculative decoding is a technique which improves inter-token latency in memory
 Speculating with a draft model
 ------------------------------
 
-The following code configures vLLM to use speculative decoding with a draft model, speculating 5 tokens at a time.
+The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time.
 
 .. code-block:: python
 
     from vllm import LLM, SamplingParams
-    
+
     prompts = [
         "The future of AI is",
     ]
     sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
-    
+
     llm = LLM(
         model="facebook/opt-6.7b",
         tensor_parallel_size=1,
@@ -33,12 +33,56 @@ The following code configures vLLM to use speculative decoding with a draft mode
         use_v2_block_manager=True,
     )
     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}")
 
+To perform the same with an online mode launch the server:
+
+.. code-block:: bash
+
+    python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 --model facebook/opt-6.7b \
+    --seed 42 -tp 1 --speculative_model facebook/opt-125m --use-v2-block-manager \
+    --num_speculative_tokens 5 --gpu_memory_utilization 0.8
+
+ Then use a client:
+
+.. code-block:: python
+
+    from openai import OpenAI
+
+    # Modify OpenAI's API key and API base to use vLLM's API server.
+    openai_api_key = "EMPTY"
+    openai_api_base = "http://localhost:8000/v1"
+
+    client = OpenAI(
+        # defaults to os.environ.get("OPENAI_API_KEY")
+        api_key=openai_api_key,
+        base_url=openai_api_base,
+    )
+
+    models = client.models.list()
+    model = models.data[0].id
+
+    # Completion API
+    stream = False
+    completion = client.completions.create(
+        model=model,
+        prompt="The future of AI is",
+        echo=False,
+        n=1,
+        stream=stream,
+    )
+
+    print("Completion results:")
+    if stream:
+        for c in completion:
+            print(c)
+    else:
+        print(completion)
+
 Speculating by matching n-grams in the prompt
 ---------------------------------------------
 
@@ -48,12 +92,12 @@ matching n-grams in the prompt. For more information read `this thread. <https:/
 .. code-block:: python
 
     from vllm import LLM, SamplingParams
-    
+
     prompts = [
         "The future of AI is",
     ]
     sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
-    
+
     llm = LLM(
         model="facebook/opt-6.7b",
         tensor_parallel_size=1,
@@ -63,7 +107,7 @@ matching n-grams in the prompt. For more information read `this thread. <https:/
         use_v2_block_manager=True,
     )
     outputs = llm.generate(prompts, sampling_params)
-    
+
     for output in outputs:
         prompt = output.prompt
         generated_text = output.outputs[0].text
@@ -74,7 +118,7 @@ Speculating using MLP speculators
 
 The following code configures vLLM to use speculative decoding where proposals are generated by
 draft models that conditioning draft predictions on both context vectors and sampled tokens.
-For more information see `this blog <https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/>`_ or 
+For more information see `this blog <https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/>`_ or
 `this technical report <https://arxiv.org/abs/2404.19124>`_.
 
 .. code-block:: python
@@ -100,9 +144,9 @@ For more information see `this blog <https://pytorch.org/blog/hitchhikers-guide-
         generated_text = output.outputs[0].text
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 
-Note that these speculative models currently need to be run without tensor parallelism, although 
-it is possible to run the main model using tensor parallelism (see example above). Since the 
-speculative models are relatively small, we still see significant speedups. However, this 
+Note that these speculative models currently need to be run without tensor parallelism, although
+it is possible to run the main model using tensor parallelism (see example above). Since the
+speculative models are relatively small, we still see significant speedups. However, this
 limitation will be fixed in a future release.
 
 A variety of speculative models of this type are available on HF hub: