From 29a778415e8f1f80cb4163cc5623df9a1d16acb9 Mon Sep 17 00:00:00 2001 From: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> Date: Thu, 26 Feb 2026 11:23:58 -0600 Subject: [PATCH] remove reference to trt model generation script --- docs/docs/frigate/hardware.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs/docs/frigate/hardware.md b/docs/docs/frigate/hardware.md index cd3f543b5..90707bcf8 100644 --- a/docs/docs/frigate/hardware.md +++ b/docs/docs/frigate/hardware.md @@ -41,8 +41,8 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat | Name | Capabilities | Notes | | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- | | Beelink EQ13 (Amazon) | Can run object detection on several 1080p cameras with low-medium activity | Dual gigabit NICs for easy isolated camera network. | -| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP) | Can handle a large number of 1080p cameras with high activity | | -| Intel 125H ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM) | Can handle a significant number of 1080p cameras with high activity | Includes NPU for more efficient detection in 0.17+ | +| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP)) | Can handle a large number of 1080p cameras with high activity | | +| Intel 125H ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM)) | Can handle a significant number of 1080p cameras with high activity | Includes NPU for more efficient detection in 0.17+ | ## Detectors @@ -172,7 +172,7 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp | Intel Arc A380 | ~ 6 ms | | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms | | | Intel Arc A750 | ~ 4 ms | | 320: ~ 8 ms | | | -### TensorRT - Nvidia GPU +### Nvidia GPUs Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA libraries. @@ -182,8 +182,6 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU. -There are improved capabilities in newer GPU architectures that TensorRT can benefit from, such as INT8 operations and Tensor cores. The features compatible with your hardware will be optimized when the model is converted to a trt file. Currently the script provided for generating the model provides a switch to enable/disable FP16 operations. If you wish to use newer features such as INT8 optimization, more work is required. - #### Compatibility References: [NVIDIA TensorRT Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/getting-started/support-matrix.html)