[Docs] Add info on when to use which PTQ/Sparsification (#1157)

SUMMARY:
Current README shows which algo we support + how to run. However, to a
user it is still hard to understand when to use which. Add more info on
based on the users use-case and hardware the optimization to apply.

TEST PLAN:
N/A

Signed-off-by: Brian Dellabetta <[email protected]>

README.md

@@ -23,6 +23,32 @@
 * SmoothQuant
 * SparseGPT
 
+### When to Use Which Optimization
+
+#### PTQ
+PTQ is performed to reduce the precision of quantizable weights (e.g., linear layers) to a lower bit-width. Supported formats are:
+
+##### [W4A16](./examples/quantization_w4a16/README.md)
+- Uses GPTQ to compress weights to 4 bits. Requires calibration dataset.
+- Useful speed ups in low QPS regimes with more weight compression. 
+- Recommended for any GPUs types. 
+##### [W8A8-INT8](./examples/quantization_w8a8_int8/README.md)
+- Uses channel-wise quantization to compress weights to 8 bits using GPTQ, and uses dynamic per-token quantization to compress activations to 8 bits. Requires calibration dataset for weight quantization. Activation quantization is carried out during inference on vLLM.
+- Useful for speed ups in high QPS regimes or offline serving on vLLM. 
+- Recommended for NVIDIA GPUs with compute capability <8.9 (Ampere, Turing, Volta, Pascal, or older). 
+##### [W8A8-FP8](./examples/quantization_w8a8_fp8/README.md)
+- Uses channel-wise quantization to compress weights to 8 bits, and uses dynamic per-token quantization to compress activations to 8 bits. Does not require calibration dataset. Activation quantization is carried out during inference on vLLM.
+- Useful for speed ups in high QPS regimes or offline serving on vLLM. 
+- Recommended for NVIDIA GPUs with compute capability >8.9 (Hopper and Ada Lovelace). 
+
+#### Sparsification
+Sparsification reduces model complexity by pruning selected weight values to zero while retaining essential weights in a subset of parameters. Supported formats include:
+
+##### [2:4-Sparsity with FP8 Weight, FP8 Input Activation](./examples/sparse_2of4_quantization_fp8/README.md)
+- Uses (1) semi-structured sparsity (SparseGPT), where, for every four contiguous weights in a tensor, two are set to zero. (2) Uses channel-wise quantization to compress weights to 8 bits and dynamic per-token quantization to compress activations to 8 bits.
+- Useful for better inference than W8A8-fp8, with almost no drop in its evaluation score [blog](https://neuralmagic.com/blog/24-sparse-llama-fp8-sota-performance-for-nvidia-hopper-gpus/). Note: Small models may experience accuracy drops when the remaining non-zero weights are insufficient to recapitulate the original distribution.
+- Recommended for compute capability >8.9 (Hopper and Ada Lovelace).
+
 
 ## Installation
 
@@ -35,16 +61,16 @@ pip install llmcompressor
 ### End-to-End Examples
 
 Applying quantization with `llmcompressor`:
-* [Activation quantization to `int8`](examples/quantization_w8a8_int8)
-* [Activation quantization to `fp8`](examples/quantization_w8a8_fp8)
-* [Weight only quantization to `int4`](examples/quantization_w4a16)
-* [Quantizing MoE LLMs](examples/quantizing_moe)
-* [Quantizing Vision-Language Models](examples/multimodal_vision)
-* [Quantizing Audio-Language Models](examples/multimodal_audio)
+* [Activation quantization to `int8`](examples/quantization_w8a8_int8/README.md)
+* [Activation quantization to `fp8`](examples/quantization_w8a8_fp8/README.md)
+* [Weight only quantization to `int4`](examples/quantization_w4a16/README.md)
+* [Quantizing MoE LLMs](examples/quantizing_moe/README.md)
+* [Quantizing Vision-Language Models](examples/multimodal_vision/README.md)
+* [Quantizing Audio-Language Models](examples/multimodal_audio/README.md)
 
 ### User Guides
 Deep dives into advanced usage of `llmcompressor`:
-* [Quantizing with large models with the help of `accelerate`](examples/big_models_with_accelerate)
+* [Quantizing with large models with the help of `accelerate`](examples/big_models_with_accelerate/README.md)
 
 
 ## Quick Tour