Selfhosted

USB is not good as its a huge bottleneck, and most external accelerators are (was?) passive without any ram onboard. Google Coral was/is 'passive' in that it resends all data all the time over the interface - Npu<->system ram.

I were going to recommend these: https://shop.geniatech.com/product/m2-ai-inference-acceleration-module/

40tops, ARM + Npu + 16gb ddr4 - a whole little Inference computer on a NVME interface. Kinara (Ara240) is a homegrown Chinese chip. While they are usually selling b2b, you can ask anyway. YMMW atmo. Also note that some NPU's are less efficient at llm's vs vision.

..but I see that they also rose almost 4* in price since I asked for, and were offered the price of 179$ ~7M ago, which is already a long time in this space. Not sure how the current ~650$ stacks up to the rest of the offers out there at the moment, but these small active AI systems on NVME are a great way of upgrading a piss-old server, enhance a new cheap 4-8port NVME mini-NAS or similar, and there's no clear bottleneck in the interface.

Look for something like this instead of Nvidia Coral and other 'passive' sticks, that are all - imho - overpriced/underperforming.

Do you want to run TensorFlow Lite / LiteRT models? PyTorch Mobile? TensorRT? onnx? YOLO? vLLM? Something else? The recommendations will vary based on your use case.

Google Coral was decent for TensorFlow Lite, but it's EOL (end of life) now. I've got the dual TPU Mini PCIe version in my home server, via a PCIe adapter board. I use it for object detection with Blue Iris + CodeProject AI and it works pretty well for that use case.

Hailo-8 is supposed to be like a more powerful version of the Coral, but I don't have experience with it. It supports a bunch of frameworks: TensorFlow, TensorFlow Lite, Keras, PyTorch and ONNX. I'd be interested in hearing other people's thoughts on it.

I don't know if any of these work over USB though. They're usually internal devices. Google marketed the Coral USB as being for development and testing only, pointing people to the M.2 and PCIe versions for production usage.

As for something totally different... There's the Nvidia Jetson single board computer which supports TensorRT, but I don't have experience with it either. I also think it's a bit older too. You could also consider getting a newer mini PC with a AMD Ryzen AI processor in it, or an Nvidia DGX Spark.

Google's latest TPUs are only available in their cloud - they're not selling the hardware to end users any more.

google coral is what you need, there's also an m.2 pcie version, also intel igpu also has limited AI capabilities through openvino, i use it to accelerate AI features in Immich (face and object recognition) on my N100 based NAS, but it's a bit too slow for Frigate realtime object recognition for example

You should probably have a look at the software you intend to run and see what kind of external NPU they support. I'm aware of Frigate for example which leverages hardware acceleration to classify camera surveillance footage. Guess their community knows which stick to get. Or you'd have a look at the frameworks which package machine learning models for inference on some NPU and have a look at their table of supported hardware.