Update README (pytorch#823)

* Update README

* Update README.md

* Update README.md

* Update README.md

* Update README.md

README.md

@@ -9,7 +9,7 @@
 torchao: PyTorch library for custom data types & optimizations. Quantize and sparsify weights, gradients, optimizers & activations for inference and training.
 
 From the team that brought you the fast series
-* 8x speedups for Image segmentation models with [sam-fast](https://pytorch.org/blog/accelerating-generative-ai) (9.5x with int8 dynamic quantization + 2:4 sparsity)
+* 9.5x speedups for Image segmentation models with [sam-fast](https://pytorch.org/blog/accelerating-generative-ai)
 * 10x speedups for Language models with [gpt-fast](https://pytorch.org/blog/accelerating-generative-ai-2)
 * 3x speedup for Diffusion models with [sd-fast](https://pytorch.org/blog/accelerating-generative-ai-3)
 
@@ -39,6 +39,8 @@ quantize_(m, int4_weight_only())
 
 For gpt-fast `int4_weight_only()` is the best option at bs=1 as it **2x the tok/s and reduces the VRAM requirements by about 65%** over a torch.compiled baseline.
 
+If you don't have enough VRAM to quantize your entire model on GPU and you find CPU quantization to be too slow then you can use the device argument like so `quantize_(model, int8_weight_only(), device="cuda")` which will send and quantize each layer individually to your GPU.
+
 If you see slowdowns with any of these techniques or you're unsure which option to use, consider using [autoquant](./torchao/quantization/README.md#autoquantization) which will automatically profile layers and pick the best way to quantize each layer.
 
 ```python
@@ -173,7 +175,7 @@ USE_CPP=0 pip install -e .
 
 We're also fortunate to be integrated into some of the leading open-source libraries including
 1. Hugging Face transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865)
-2. Hugging Face diffusers with a minimal example thanks to [Sayak Paul](https://www.linkedin.com/posts/sayak-paul_want-to-combine-quantization-and-benefit-activity-7231950868605022208-g52d?utm_source=share&utm_medium=member_desktop)
+2. Hugging Face diffusers best practices with torch.compile and torchao [standalone repo](https://github.com/sayakpaul/diffusers-torchao)
 3. Mobius HQQ backend leveraged our int4 kernels to get [195 tok/s on a 4090](https://github.com/mobiusml/hqq#faster-inference)
 
 ## Videos

torchao/quantization/README.md

@@ -33,8 +33,7 @@ And a quick crash course on inference quantization to help parse the above table
 
 ## Autoquantization
 
-The `autoquant` api can be used to quickly and accurately quantize your model. When used as in the example below, the api first identifies the shapes
-of the activations that the different linear layers see, it then benchmarks these shapes across different types of quantized and non-quantized layers in order to pick the fastest one, attempting to take into account fusions where possible. Finally once the best class is found for each layer, it swaps the linear. By default the api only uses int8 techniques, i.e. it chooses between no quantization, int8 dynamic quantization and int8 weight only quantization for each layer, though there is also an option add int4 quantization which can be used for maximum performance or to avoid perf regressions from `int4_weight_only()`.
+When used as in the example below, autoquant first identifies the shapes of the activations that the different linear layers see, it then benchmarks these shapes across different types of quantized and non-quantized layers in order to pick the fastest one, attempting to take into account fusions where possible. Finally once the best class is found for each layer, it swaps the linear. By default the api only uses int8 techniques, i.e. it chooses between no quantization, int8 dynamic quantization and int8 weight only quantization for each layer, though there is also an option add int4 quantization which can be used for maximum performance or to avoid perf regressions from `int4_weight_only()`.
 
 ```python
 import torch