diff --git a/docs/docs/configuration/license_plate_recognition.md b/docs/docs/configuration/license_plate_recognition.md
index a18c822f5..5f70dd9a0 100644
--- a/docs/docs/configuration/license_plate_recognition.md
+++ b/docs/docs/configuration/license_plate_recognition.md
@@ -68,8 +68,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
   - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
   - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
 - **`device`**: Device to use to run license plate detection _and_ recognition models.
-  - Default: `CPU`
-  - This can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
+  - Default: `None`
+  - This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
 - **`model_size`**: The size of the model used to identify regions of text on plates.
   - Default: `small`
   - This can be `small` or `large`.
@@ -432,6 +432,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
 
 If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
 
-### I see "Error running ... model" in my logs. How can I fix this?
+### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
 
 This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.