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README.md

@@ -32,8 +32,8 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.479 ms | 0 - 2 MB | FP16 | NPU |  [QuickSRNetLarge.tflite](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.103 ms | 0 - 67 MB | FP16 | NPU |  [QuickSRNetLarge.so](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.492 ms | 0 - 8 MB | FP16 | NPU |  [QuickSRNetLarge.tflite](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 2.101 ms | 0 - 5 MB | FP16 | NPU |  [QuickSRNetLarge.so](https://huggingface.co/qualcomm/QuickSRNetLarge/blob/main/QuickSRNetLarge.so) 
 
 
 ## Installation
@@ -90,23 +90,6 @@ device. This script does the following:
 python -m qai_hub_models.models.quicksrnetlarge.export
 ```
 
-```
-Profile Job summary of QuickSRNetLarge
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 1.72 ms
-Estimated Peak Memory Range: 0.02-26.78 MB
-Compute Units: NPU (28),CPU (3) | Total (31)
-
-Profile Job summary of QuickSRNetLarge
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 1.49 ms
-Estimated Peak Memory Range: 0.20-17.20 MB
-Compute Units: NPU (31) | Total (31)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/QuickSRNetLarge/export.py)