2026-09-08 07:24 UTC
Replies (5)
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@maniel@sopuli.xyz 2026-09-08 07:39
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
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@dan@upvote.au 2026-09-08 08:17
Do you want to run TensorFlow Lite / LiteRT models? PyTorch Mobile? onnx? YOLO? Something else? 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.
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@Sims@lemmy.ml 2026-09-08 09:03
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 - Npusystem ram. I were going to recommend these: shop.geniatech.com/…/m2-ai-inference-acceleration… 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.
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@Faceman2K23@discuss.tchncs.de 2026-09-09 05:59
if all you need is a tiny NPU for things like image object identification or OCR the IGPU you already have will be better. if you need slightly more than that, but not as much power as a large dedicated gpu, then you would have to look at a newer mini-pc with a more modern chip. I do immich face/object detection and Birdnet audio analysis on the igpu of an intel 14600 and it’s fine, tiny amount of usage and plenty fast. my other node has a core ultra 235 and the igpu is faster than the on-chip npu in every regard, the npu is also a bit harder to actually get working and put to use.
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@tired_n_bored@lemmy.world 2026-09-09 08:16
Can you tell us what atually will you need? Don’t forget that most of the time applications will use your bare CPU to perform AI whereas you can use the integrated GPU for better performances.