Regarding Google Coral, from the docs: https://docs.frigate.video/frigate/hardware

The Coral is no longer recommended for new Frigate installations, except in deployments with particularly low power requirements or hardware incapable of utilizing alternative AI accelerators for object detection. Instead, we suggest using one of the numerous other supported object detectors. Frigate will continue to provide support for the Coral TPU for as long as practicably possible given its still one of the most power-efficient devices for executing object detection models.

Personally, I'd recommend OpenVINO. It works on iGPUs, and if that's not possible it's quite performant on CPUs. I currently run a Google Coral and the Linux driver is no longer officially maintained meaning it breaks every now and then after a kernel update, next time it breaks I'm going with OpenVINO.

Also, I'd install Docker in an LXC for Frigate, it makes passing the iGPU through to it a lot easier.

You can still get the Google Coral (both the USB dongle and M.2/PCIe versions), but Google has effectively abandoned the official drivers. You'll need to use community-maintained DKMS drivers if you go with the PCIe version on newer Linux kernels.

If you want a modern alternative, look into the Hailo-8 or Hailo-8L M.2 AI accelerators. They plug right into an open M.2 slot and natively integrate into recent versions of Frigate. Keep in mind that these accelerators ONLY process object detection inference - they do not handle video decoding.

For video decoding, your OptiPlex 7070's Intel CPU has Quick Sync Video (QSV). You can pass /dev/dri through to your container, and Intel iGPU hardware acceleration will handle decoding the RTSP streams with minimal CPU overhead.

If you prefer an all-in-one approach, an Intel or Nvidia GPU can handle both video decoding and object detection (via OpenVINO or TensorRT), but a Micro PC limits your physical expansion options.

Storing recordings on a TrueNAS share over NFS or SMB works just fine. Mount the NFS share directly in Proxmox or the VM host, then pass the directory through to your Docker container.

In my experience frigate is quite CPU intensive. I have 10 cameras and my old I7-8700 would die trying to have frigate process them all.

I opted for a different approach with LazyNVR (https://codeberg.org/LazyNVR/lazynvr-sources) leveraging the capability of the camera to perform motion detection and video recording directly. This offloaded my CPU and now I can record all 10 cameras on a OPi3.

I have the same setup. Run ov on the igpu, works great.