Revert "[Doc] Update supported_hardware.rst (#7276)" (#7467)

Author: WoosukKwon

docs/source/quantization/supported_hardware.rst

@@ -5,20 +5,18 @@ Supported Hardware for Quantization Kernels
 
 The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
 
-=====================  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
-Implementation         Volta   Turing   Ampere   Ada    Hopper  AMD GPU  Intel GPU  x86 CPU  AWS Inferentia  Google TPU
-=====================  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
-AWQ                    ❌      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-GPTQ                   ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-Marlin (GPTQ/AWQ/FP8)  ❌      ❌       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-INT8 (W8A8)            ❌      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-FP8 (W8A8)             ❌      ❌       ❌       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-AQLM                   ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-bitsandbytes           ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-DeepSpeedFP            ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-GGUF                   ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-SqueezeLLM             ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
-=====================  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
+==============  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
+Implementation  Volta   Turing   Ampere   Ada    Hopper  AMD GPU  Intel GPU  x86 CPU  AWS Inferentia  Google TPU
+==============  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
+AQLM            ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+AWQ             ❌      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+DeepSpeedFP     ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+FP8             ❌      ❌       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+Marlin          ❌      ❌       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+GPTQ            ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+SqueezeLLM      ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+bitsandbytes    ✅      ✅       ✅       ✅     ✅      ❌        ❌         ❌       ❌              ❌
+==============  ======  =======  =======  =====  ======  =======  =========  =======  ==============  ==========
 
 Notes:
 ^^^^^^
@@ -29,4 +27,4 @@ Notes:
 
 Please note that this compatibility chart may be subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods.
 
-For the most up-to-date information on hardware support and quantization methods, please check the `quantization directory <https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization>`_ or consult with the vLLM development team.
+For the most up-to-date information on hardware support and quantization methods, please check the `quantization directory <https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization>`_ or consult with the vLLM development team.