Qwen3.5‑4B Embeds NES 6502 CPU, Runs Super Mario in Slow Mode
Current overview (not historical)
Drew Breunig has embedded a NES 6502 CPU emulator directly into the weights of the Qwen3.5‑4B language model, enabling the model to execute classic ROMs such as Super Mario. The emulator runs at a very modest speed, but the proof‑of‑concept demonstrates that large‑scale neural nets can host functional hardware simulations. The project was inspired by the earlier qwenputer experiment and was shared by Breunig on Hacker News using an Opus 5.5 prompt. While the emulator’s performance is limited, the work highlights the potential for future research into integrating more complex hardware logic within AI model parameters.
Written with AI from the reporting · updated 10/30/2026
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- 1Unclassified fact stationOct 30, 00:00recorded Oct 31, 06:58single sourcerevised
Qwen3.5-4B 中嵌入 NES 6502 CPU 运行超级马里奥
Read original source ↗ Simon WillisonReplay this moment in the explorerEvidence & revisions · 1 source records
- Subject：
- qwen
- Action：
- 嵌入 NES 6502 CPU 权重
- Object：
- NES ROM 运行
- Occurred (fact side)：
- Oct 30, 00:00
- Published (valid side)：
- Oct 31, 05:59
- First seen (known side)：
- Oct 31, 06:58
Fact stationSingle source · one-sided claimRevisedCopy may have been revised later; time replay is bounded. Layer labels group reading only, not causation between layers.
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- Simon WillisonRelated fact: single sourceSource report revisedDrew Breunig Embeds a Working NES 6502 Emulator Inside Qwen3.5‑4B
Report first recorded Oct 31, 06:58
On 30 October 2026, Drew Breunig announced that he has integrated a functional Nintendo Entertainment System (NES) 6502 CPU emulator into the weight matrix of the Qwen3.5‑4B language model. The emulator can run NES ROMs—including a very slow version of Super Mario Bros—directly within the model’s inference engine. Breunig revealed the Opus 5.5 prompts he used to construct the system and cited the 15‑year‑old conformance test suite from Christopher Pow as a key resource. The project demonstrates that large language models can host low‑level hardware emulation logic, opening a new avenue for embedding executable code within neural network weights.
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