diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 20a2d37540a..cf117623910 100755
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -93,6 +93,10 @@ Changelog
 **Deprecations**
 
 - Deprecate the public ``QuantizationArgumentsWithConfig`` name in ``modelopt.torch.quantization.plugins.transformers_trainer``; it now aliases ``QuantizationArguments`` and will be removed in a future release.
+- Deprecate ``examples/llm_autodeploy``. The AutoQuant + TensorRT-LLM AutoDeploy
+  workflow it demonstrates will be removed in a future release; use TensorRT-LLM's
+  `AutoDeploy <https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/auto_deploy>`_
+  directly together with ModelOpt PTQ in ``examples/llm_ptq``.
 
 **Bug Fixes**
 
diff --git a/examples/llm_autodeploy/README.md b/examples/llm_autodeploy/README.md
index c21f8c203f4..f477f80d778 100644
--- a/examples/llm_autodeploy/README.md
+++ b/examples/llm_autodeploy/README.md
@@ -1,5 +1,12 @@
 # Deploy AutoQuant Models with AutoDeploy
 
+> [!WARNING]
+> **Deprecated (ModelOpt 0.45).** This example is deprecated and will be removed in
+> a future release (0.46). For mixed-precision deployment, use TensorRT-LLM's
+> [AutoDeploy](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/auto_deploy)
+> directly, combined with ModelOpt PTQ/AutoQuant from
+> [`examples/llm_ptq`](../llm_ptq/README.md).
+
 This guide demonstrates how to deploy mixed-precision models using ModelOpt's AutoQuant and TRT-LLM's AutoDeploy.
 
 [ModelOpt's AutoQuant](https://nvidia.github.io/Model-Optimizer/reference/generated/modelopt.torch.quantization.model_quant.html#modelopt.torch.quantization.model_quant.auto_quantize) is a post-training quantization (PTQ) algorithm that optimizes model quantization by selecting the best quantization format for each layer while adhering to user-defined compression constraints. This approach allows users to balance model accuracy and performance effectively.