Add Spinquant benchmark results to README

torchao/prototype/spinquant/README.md

@@ -4,8 +4,30 @@ Re-implementation of SpinQuant based on the official code implementation (https:
 
 ## Usage
 
-Using this implementation with CUDA requires installing the Fast Hadamard Transform CUDA package, which can be done as follows:
+For optimal performance on CUDA GPUs, install the Fast Hadamard Transform package:
 
 ```shell
 pip install git+https://github.com/Dao-AILab/fast-hadamard-transform.git
-```
+```
+
+## Performance 
+
+See https://github.com/pytorch/ao/pull/983 for Wikitext benchmark results.
+
+Without `torch.compile`:
+
+| Configuration  | Average tokens/sec | Average Bandwidth (GB/s) | Peak Memory Usage (GB) | Model Size (GB) |
+|----------------|--------------------|--------------------------|------------------------|-----------------|
+| Baseline       | 27.33              | 361.21                   | 13.62                  | 13.21           |
+| Spinquant (R4) | 23.01              | 304.10                   | 14.24                  | 13.22           |
+
+With `torch.compile`:
+
+| Configuration        | Average tokens/sec | Average Bandwidth (GB/s) | Peak Memory Usage (GB) | Model Size (GB) |
+|----------------------|--------------------|--------------------------|------------------------|-----------------|
+| Baseline             | 114.08             | 1507.58                  | 13.88                  | 13.21           |
+| Spinquant (R4)       | 109.59             | 1448.61                  | 13.72                  | 13.22           |
+| Spinquant (R1+R2+R4) | 109.64             | 1449.28                  | 14.90                  | 13.22           |
+
+
+NB: R1 and R2 are fused into the linear weights before inference takes place, so it is expected that they do not lead to additional overhead at inference time.