Fsbonetto

I just ran an LLM on an FPGA. Now I'm wondering: how far are we from building our own TPU? Not in simulation. Not on a GPU. On a real Xilinx Kintex-7 FPGA with 4GB RAM. A few days ago, I came across openTPU, an open-source AI accelerator project created by Felipe Sens Bonettoe with the help of AI agents. The idea was too interesting to leave on GitHub. So I decided to put it to the test. 🔧 From GitHub to real hardware: → Connected the FPGA board to MINISFORUM PC via PCIe → Configured the hardware → Built the software stack → Brought up the DMA drivers → Loaded real model weights → Started testing inference And it actually worked. ✅ FPGA hardware detected and initialized ✅ PCIe DMA communication working ✅ Memory subsystem operational ✅ Hardware self-tests passing ✅ Real LLM inference running, including Qwen The most interesting part wasn't the performance. It was realizing that you can take an open-source accelerator architecture, bring it onto real hardware, and start experimenting with your own AI inference engine. Of course, this isn't an H100 competitor. And an FPGA prototype is still a long way from a production ASIC. But that's exactly why I find it exciting. 🔬 What's next? A few years ago, building your own AI accelerator sounded like something only a semiconductor company could attempt. Today, open-source hardware, affordable FPGA boards, and AI-assisted engineering are making that idea worth experimenting with. Maybe the next wave of AI innovation won't just be about building better models. Maybe it will also be about who gets to build the hardware that runs them. 👏 Huge credit to Felipe Sens Bonetto for creating openTPU. nitter.gq/reasoningTokens 🔗 Original project: github.com/FeSens/openTPU #FPGA #AIHardware #OpenSource #ASIC #LLM