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

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docs/source/quantization/supported_hardware.rst
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@ -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:
^^^^^^