diff --git a/docs_new/docs.json b/docs_new/docs.json
index b385d61ee..31528ce92 100644
--- a/docs_new/docs.json
+++ b/docs_new/docs.json
@@ -710,7 +710,8 @@
               "docs/basic_usage/ollama_api",
               "docs/basic_usage/offline_engine_api",
               "docs/basic_usage/native_api",
-              "docs/basic_usage/sampling_params"
+              "docs/basic_usage/sampling_params",
+              "docs/basic_usage/aws_sagemaker"
             ]
           },
           {
diff --git a/docs_new/docs/basic_usage/aws_sagemaker.mdx b/docs_new/docs/basic_usage/aws_sagemaker.mdx
new file mode 100644
index 000000000..63deef77f
--- /dev/null
+++ b/docs_new/docs/basic_usage/aws_sagemaker.mdx
@@ -0,0 +1,142 @@
+---
+title: "Amazon SageMaker AI"
+description: "Deploy SGLang on Amazon SageMaker AI endpoints using the AWS Deep Learning Container."
+---
+
+Deploy SGLang on [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/) endpoints using the
+[AWS Deep Learning Container (DLC)](https://aws.github.io/deep-learning-containers/sglang/) for SGLang.
+The SageMaker image variant accepts model configuration via environment variables and serves on port 8080.
+
+This guide uses the pre-built DLC image. To build and deploy your own container instead, see
+[Method 7: Run on AWS SageMaker](/docs/get-started/install#more-3) in the installation guide.
+
+## Container image
+
+AWS publishes pre-built, security-patched SGLang DLCs. The SageMaker GPU image is available from the
+Amazon ECR registry (account `763104351884`) in each supported region. For example, in `us-west-2`:
+
+```text
+763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0
+```
+
+For the full list of image tags, see the
+[Available DLC Images](https://aws.github.io/deep-learning-containers/reference/available_images/) reference,
+and for region-specific account IDs and supported regions, see
+[Region Availability](https://aws.github.io/deep-learning-containers/reference/region_availability/).
+
+## Specifying the model
+
+The SageMaker image resolves the model in this order:
+
+1. **`SM_SGLANG_MODEL_PATH` environment variable** — explicit Hugging Face ID or path.
+2. **`/opt/ml/model`** — when SageMaker mounts model artifacts via `ModelDataUrl` or `ModelDataSource`,
+   the entrypoint uses this path by default.
+
+For gated models, also pass `HF_TOKEN`.
+
+Any `SM_SGLANG_*` environment variable is converted to a `--<name>` SGLang server argument
+(for example, `SM_SGLANG_CONTEXT_LENGTH=4096` becomes `--context-length 4096`).
+
+## Deploy with the SageMaker Python SDK
+
+```python
+from sagemaker.model import Model
+from sagemaker.predictor import Predictor
+from sagemaker.serializers import JSONSerializer
+
+model = Model(
+    image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0",
+    role="arn:aws:iam::<account_id>:role/<role_name>",
+    predictor_cls=Predictor,
+    env={"SM_SGLANG_MODEL_PATH": "openai/gpt-oss-20b"},
+)
+
+predictor = model.deploy(
+    instance_type="ml.g5.2xlarge",
+    initial_instance_count=1,
+    inference_ami_version="al2023-ami-sagemaker-inference-gpu-4-1",
+    serializer=JSONSerializer(),
+)
+
+response = predictor.predict({
+    "model": "openai/gpt-oss-20b",
+    "messages": [{"role": "user", "content": "What is deep learning?"}],
+    "max_tokens": 256,
+})
+print(response)
+
+# Cleanup
+predictor.delete_model()
+predictor.delete_endpoint(delete_endpoint_config=True)
+```
+
+## Deploy with Boto3
+
+```python
+import json
+import boto3
+
+sm = boto3.client("sagemaker")
+smrt = boto3.client("sagemaker-runtime")
+
+sm.create_model(
+    ModelName="sglang-model",
+    PrimaryContainer={
+        "Image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0",
+        "Environment": {"SM_SGLANG_MODEL_PATH": "openai/gpt-oss-20b"},
+    },
+    ExecutionRoleArn="arn:aws:iam::<account_id>:role/<role_name>",
+)
+
+sm.create_endpoint_config(
+    EndpointConfigName="sglang-config",
+    ProductionVariants=[{
+        "VariantName": "default",
+        "ModelName": "sglang-model",
+        "InstanceType": "ml.g5.2xlarge",
+        "InitialInstanceCount": 1,
+        "InferenceAmiVersion": "al2023-ami-sagemaker-inference-gpu-4-1",
+    }],
+)
+
+sm.create_endpoint(EndpointName="sglang-endpoint", EndpointConfigName="sglang-config")
+sm.get_waiter("endpoint_in_service").wait(EndpointName="sglang-endpoint")
+
+resp = smrt.invoke_endpoint(
+    EndpointName="sglang-endpoint",
+    ContentType="application/json",
+    Body=json.dumps({
+        "model": "openai/gpt-oss-20b",
+        "messages": [{"role": "user", "content": "What is deep learning?"}],
+        "max_tokens": 256,
+    }),
+)
+print(json.loads(resp["Body"].read()))
+
+# Cleanup
+sm.delete_endpoint(EndpointName="sglang-endpoint")
+sm.delete_endpoint_config(EndpointConfigName="sglang-config")
+sm.delete_model(ModelName="sglang-model")
+```
+
+## Model artifacts
+
+When `ModelDataUrl` (or `ModelDataSource`) points to a tarball or S3 prefix, SageMaker mounts the contents
+at `/opt/ml/model`. The entrypoint defaults `--model-path` to that location, so `SM_SGLANG_MODEL_PATH`
+can be omitted:
+
+```text
+model.tar.gz
+├── config.json              # standard model files (Hugging Face layout)
+├── tokenizer.json
+└── *.safetensors
+```
+
+## Notes
+
+- GPU deployments require `inference_ami_version` — the default SageMaker host AMI has incompatible NVIDIA
+  drivers for CUDA 13 images. See the
+  [ProductionVariant API reference](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html)
+  for valid values.
+- The endpoint exposes an OpenAI-compatible API, so the request body matches the SGLang server's
+  `/v1/chat/completions` schema.
diff --git a/docs_new/docs/get-started/install.mdx b/docs_new/docs/get-started/install.mdx
index 6bc70d421..e334eb718 100644
--- a/docs_new/docs/get-started/install.mdx
+++ b/docs_new/docs/get-started/install.mdx
@@ -167,7 +167,9 @@ sky status --endpoint 30000 sglang
 
 To deploy on SGLang on AWS SageMaker, check out [AWS SageMaker Inference](https://aws.amazon.com/sagemaker/ai/deploy)
 
-Amazon Web Services provide supports for SGLang containers along with routine security patching. For available SGLang containers, check out [AWS SGLang DLCs](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#sglang-containers)
+Amazon Web Services provide supports for SGLang containers along with routine security patching. For available SGLang containers, check out [AWS SGLang DLCs](https://aws.github.io/deep-learning-containers/reference/available_images/#sglang).
+
+To deploy a pre-built SGLang Deep Learning Container without building your own image, see [Amazon SageMaker AI](/docs/basic_usage/aws_sagemaker).
 
 To host a model with your own container, follow the following steps: