Docs / Quantization: refactor quantization documentation (#30942)

* refactor quant docs

* delete file

* rename to overview

* fix

* fix table

* fix

* add content

* fix library versions

* fix table

* fix table

* fix table

* fix table

* Apply suggestions from code review

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* replace to quantization_config

* fix aqlm snippet

* add DLAI courses

* fix

* fix table

* fix bulet points

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/en/quantization/optimum.md

@@ -0,0 +1,19 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
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+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+
+-->
+
+# Optimum
+
+The [Optimum](https://huggingface.co/docs/optimum/index) library supports quantization for Intel, Furiosa, ONNX Runtime, GPTQ, and lower-level PyTorch quantization functions. Consider using Optimum for quantization if you're using specific and optimized hardware like Intel CPUs, Furiosa NPUs or a model accelerator like ONNX Runtime.