Revert "[Doc] Update supported_hardware.rst (vllm-project#7276)" (vll…

…m-project#7467)

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.