AI model gets a 32-bit RISC-V CPU compiled directly into its weights, then runs DOOM on it

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AI model gets a 32-bit RISC-V CPU compiled directly into its weights, then runs DOOM on it

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QWenputer by @divagr1925 is so cool! but… can it run DOOM?
Ofc yes! E1M1 on the RISC-V CPU compiled into Qwen3.5-4B weights. will opensource too https://t.co/hEa46rD6CJ October 29, 2026

The implementation of Qwenputer raises some interesting concepts and solutions. For example, the developer used Qwen’s Gated DeltaNet (GND) state as RAM, which Agrawal reckons is “a pretty neat trick.” They admit that using these ‘fast weights’ as RAM isn’t an original concept, but modifying Qwen 3.5 this way does seem to be a first.

Unlike traditional LLM creation, adding a ‘CPU’ doesn’t involve training. This RISC-V CPU implementation “is 11 transformer layers in front of Qwen's own, and none of their weights are learned: a compiler computes every one,” it is explained. Agrawal boasts that Qwenputer’s built-in CPU “passes the official riscv-tests, its programs match qemu, and it has run 18 million instructions free-running without a single error.”

The developer stresses that Qwen makes use of this ‘CPU’ to send instructions and read them back with no external tool or runtime calls. In practice, it works as intended. Use of the CPU inside Qwenputer provides exact calculations, overcoming the arithmetic weaknesses of many LLMs. Moreover, users can run code on the CPU inside the LLM, with C and MicroPython compatibility.

Not content with wedging a CPU into an LLM, Agrawal decided to also give it a filesystem. They describe this extension of the model as relatively trivial, since RAM is already in place. Indeed, the filesystem storage also uses the DeltaNet state. There are 1,024 slots holding up to 512 files. Moreover, this storage is persistent if you save the state at the end of a conversation with the LLM.

AI coder gets Doom running on a custom CPU designed by GPT-5.6 Sol

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