[Doc] Update quantization supported hardware table (#7595)

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@ -5,25 +5,138 @@ 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
============== ====== ======= ======= ===== ====== ======= ========= ======= ============== ==========
AQLM ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
AWQ ❌ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
DeepSpeedFP ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
FP8 ❌ ❌ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
Marlin ❌ ❌ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
GPTQ ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
SqueezeLLM ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
bitsandbytes ✅ ✅ ✅ ✅ ✅ ❌ ❌ ❌ ❌ ❌
============== ====== ======= ======= ===== ====== ======= ========= ======= ============== ==========
.. list-table::
:header-rows: 1
:widths: 20 8 8 8 8 8 8 8 8 8 8
* - 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
- ✅︎
- ✅︎
- ✅︎
- ✅︎
- ✅︎
- ✗
- ✗
- ✗
- ✗
- ✗
Notes:
^^^^^^
- Volta refers to SM 7.0, Turing to SM 7.5, Ampere to SM 8.0/8.6, Ada to SM 8.9, and Hopper to SM 9.0.
- "✅" indicates that the quantization method is supported on the specified hardware.
- "❌" indicates that the quantization method is not supported on the specified hardware.
- "✅" indicates that the quantization method is supported on the specified hardware.
- "" indicates that the quantization method is not supported on the specified hardware.
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.