[Docs] Add supported quantization methods to docs (#2135)

README.md

@@ -35,6 +35,7 @@ vLLM is fast with:
 - State-of-the-art serving throughput
 - Efficient management of attention key and value memory with **PagedAttention**
 - Continuous batching of incoming requests
+- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629)
 - Optimized CUDA kernels
 
 vLLM is flexible and easy to use with:
@@ -44,7 +45,7 @@ vLLM is flexible and easy to use with:
 - Tensor parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA CUDA and AMD ROCm.
+- Support NVIDIA GPUs and AMD GPUs.
 
 vLLM seamlessly supports many Hugging Face models, including the following architectures:
 

docs/source/index.rst

@@ -30,6 +30,7 @@ vLLM is fast with:
 * State-of-the-art serving throughput
 * Efficient management of attention key and value memory with **PagedAttention**
 * Continuous batching of incoming requests
+* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_
 * Optimized CUDA kernels
 
 vLLM is flexible and easy to use with:
@@ -39,7 +40,7 @@ vLLM is flexible and easy to use with:
 * Tensor parallelism support for distributed inference
 * Streaming outputs
 * OpenAI-compatible API server
-* Support NVIDIA CUDA and AMD ROCm.
+* Support NVIDIA GPUs and AMD GPUs.
 
 For more information, check out the following: