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openTPU: AI agents build an LLM accelerator that runs on a Kintex-7 FPGA card

Felipe Sens Bonetto used AI agents to write openTPU, an LLM inference accelerator with its own instruction set, simulator, compiler and host software. On an Inspur PCIe card with a Xilinx Kintex-7 and 2 DDR3 channels it runs 10 models, including Qwen3 and Gemma 4, with their real weights. A README GIF shows the chat demo running on the card.

Open original source github.com · Felipe Sens Bonetto

Shown working
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README GIF shows otpu-chat running LFM2.5-230M on the Kintex-7 card with otpu-smi showing utilisation and DRAM bandwidth; tables of on-card decode rates measured 2026-09-29.

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Preview image for openTPU: AI agents build an LLM accelerator that runs on a Kintex-7 FPGA card
The source's own preview image, from github.com.

Briefing

The card produces the same tokens as the Python simulator bit for bit, and the tests check the RTL against that simulator under Verilator. The README's tables, measured from 29 September to 1 October on dated production images, report decode rates from about 4 tok/s for Phi-4-mini to 86 tok/s for LFM2.5-230M in 4-bit, with DRAM traffic at 82 to 94% of the DDR3 peak.

The bitstream runs at 133.33 MHz with a four-column systolic matrix unit. Mixture-of-experts models larger than the card's 4 GiB, such as Qwen3.5-35B-A3B, run with their experts streamed from host storage. The README does not name the agents.

From the GitHub API

Repository facts

Repository facts for fesens/opentpu, from the GitHub API
Repositoryfesens/opentpu
Stars753
LicenseApache-2.0 Permissive
LanguageVerilog
Last push15 Oct 2026 14 d ago

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