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update docs for avx cpu system requirements (#22222)

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Josh Hawkins
2026-03-02 20:18:33 -07:00
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commit 0dd1e94d60
7 changed files with 10 additions and 5 deletions
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docs/docs/configuration/custom_classification/object_classification.md
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@@ -7,7 +7,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
## Minimum System Requirements
Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
Object classification models are lightweight and run very fast on CPU.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
docs/docs/configuration/custom_classification/state_classification.md
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@@ -7,7 +7,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
## Minimum System Requirements
State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
State classification models are lightweight and run very fast on CPU.
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
docs/docs/configuration/face_recognition.md
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@@ -32,6 +32,8 @@ All of these features run locally on your system.
## Minimum System Requirements
A CPU with AVX instructions is required to run Face Recognition.
The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
The `large` model is optimized for accuracy, an integrated or discrete GPU / NPU is required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
docs/docs/configuration/license_plate_recognition.md
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@@ -30,7 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
## Minimum System Requirements
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM and a CPU with AVX instructions is required.
## Configuration
docs/docs/configuration/semantic_search.md
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@@ -13,7 +13,7 @@ Semantic Search is accessed via the _Explore_ view in the Frigate UI.
Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
A minimum of 8GB of RAM is required to use Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
A minimum of 8GB of RAM is required to use Semantic Search. A CPU with AVX instructions is required to run Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
For best performance, 16GB or more of RAM and a dedicated GPU are recommended.