[Doc][Serve][LLM] Add doc for deploying DeepSeek

doc/source/serve/llm/serving-llms.rst

@@ -60,7 +60,6 @@ Quickstart Examples
 -------------------
 
 
-
 Deployment through :class:`LLMRouter <ray.serve.llm.LLMRouter>`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -249,6 +248,54 @@ For deploying multiple models, you can pass a list of :class:`LLMConfig <ray.ser
             serve.run(llm_app, blocking=True)
 
 
+Example: Deploying DeepSeek
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following example shows how you can deploy DeepSeek R1 or V3:
+
+.. tab-set::
+
+    .. tab-item:: Builder Pattern
+        :sync: builder
+
+        .. code-block:: python
+
+            from ray import serve
+            from ray.serve.llm import LLMConfig, LLMRouter, LLMServer
+
+            llm_config = LLMConfig(
+                model_loading_config=dict(
+                    model_id="deepseek",
+                    # Change to model download path
+                    model_source="/path/to/the/model",
+                ),
+                deployment_config=dict(autoscaling_config=dict(
+                    min_replicas=1,
+                    max_replicas=1,
+                )),
+                # Change to the accelerator type of the node
+                accelerator_type="H100",
+                runtime_env=dict(env_vars=dict(VLLM_USE_V1="1")),
+                # Customize engine arguments as needed (e.g. vLLM engine kwargs)
+                engine_kwargs=dict(
+                    tensor_parallel_size=8,
+                    pipeline_parallel_size=2,
+                    gpu_memory_utilization=0.92,
+                    dtype="auto",
+                    max_num_seqs=40,
+                    max_model_len=16384,
+                    enable_chunked_prefill=True,
+                    enable_prefix_caching=True,
+                    trust_remote_code=True,
+                ),
+            )
+
+            # Deploy the application
+            deployment = LLMServer.as_deployment(
+                llm_config.get_serve_options(name_prefix="vLLM:")).bind(llm_config)
+            llm_app = LLMRouter.as_deployment().bind([deployment])
+            serve.run(llm_app)
+
 Production Deployment
 ---------------------