diff --git a/docs/source/quantization/auto_awq.rst b/docs/source/quantization/auto_awq.rst
index 0a2b4423..bbbb9aee 100644
--- a/docs/source/quantization/auto_awq.rst
+++ b/docs/source/quantization/auto_awq.rst
@@ -3,6 +3,12 @@
 AutoAWQ
 ==================
 
+.. warning::
+
+   Please note that AWQ support in vLLM is under-optimized at the moment. We would recommend using the unquantized version of the model for better
+   accuracy and higher throughput. Currently, you can use AWQ as a way to reduce memory footprint. As of now, it is more suitable for low latency
+   inference with small number of concurrent requests. vLLM's AWQ implementation have lower throughput than unquantized version.
+
 To create a new 4-bit quantized model, you can leverage `AutoAWQ <https://github.com/casper-hansen/AutoAWQ>`_. 
 Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
 The main benefits are lower latency and memory usage.