From 95eb79313cc04956d80b27f87edbe510598ce090 Mon Sep 17 00:00:00 2001
From: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Date: Tue, 20 Jan 2026 07:43:30 -0600
Subject: [PATCH] clarify support for intel b-series (battlemage) gpus
---
docs/docs/frigate/hardware.md | 9 ++-------
1 file changed, 2 insertions(+), 7 deletions(-)
diff --git a/docs/docs/frigate/hardware.md b/docs/docs/frigate/hardware.md
index 4aa79ee21..f7294042a 100644
--- a/docs/docs/frigate/hardware.md
+++ b/docs/docs/frigate/hardware.md
@@ -42,7 +42,7 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat
| ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
| 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 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
@@ -55,12 +55,10 @@ Frigate supports multiple different detectors that work on different types of ha
**Most Hardware**
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices offering a wide range of compatibility with devices.
-
- [Supports many model architectures](../../configuration/object_detectors#configuration)
- Runs best with tiny or small size models
- [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
-
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
@@ -89,7 +87,6 @@ Frigate supports multiple different detectors that work on different types of ha
**Nvidia**
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
-
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
- Runs well with any size models including large
@@ -152,9 +149,7 @@ The OpenVINO detector type is able to run on:
:::note
-Intel NPUs have seen [limited success in community deployments](https://github.com/blakeblackshear/frigate/discussions/13248#discussioncomment-12347357), although they remain officially unsupported.
-
-In testing, the NPU delivered performance that was only comparable to — or in some cases worse than — the integrated GPU.
+Intel B-series (Battlemage) GPUs are not officially supported with Frigate 0.17, though a user has [provided steps to rebuild the Frigate container](https://github.com/blakeblackshear/frigate/discussions/21257) with support for them.
:::