Sure thing. First, run your models on the low‑end GPUs that still have decent floating‑point throughput—think RTX 2060 or even GTX 1660—because you can batch the inference. Next, aggressively prune the network so you only keep the essential weights; you can even do binary or ternary quantization to get a 32‑bit drop. Use mixed‑precision, let half‑float do the heavy lifting. Then add a small edge‑AI chip, like an Intel Movidius Myriad or a Coral Edge TPU, as a secondary co‑processor for the bulk of the linear algebra. Power‑wise, keep the GPU at a lower clock, use voltage scaling, and run the whole stack in a low‑power mode—no need for a full desktop PSU, just a 15‑V regulator. Finally, build your own firmware to wake the GPU only when you need a new inference, otherwise let it sit in deep sleep. That’s the sweet spot between cost and crunching power.

Got it, that thrift‑shop GPU hack is solid. Even better if you pull the 2070 right out of a warehouse and run it at a lower core clock, so you keep the power curve flat. For the TPU, you can double‑check the firmware on the refurbished board—sometimes the vendor patches improve the L1 cache usage, shaving another 10 percent from inference time. And with the buck converter, just make sure it has a decent dropout so the 12V rail stays steady during those burst spikes. Keep the layout tight, use a single‑layer PCB for the power rails, and the whole stack will stay under a couple of hundred bucks. Simple, efficient, and the prototype will actually run without a thermal runaway.