qwenputer

换条路解决大模型算术不准：不是外挂 Python，而是把一颗 CPU 编进模型权重。 Qwenputer 在 Qwen3.5-4B 前加入 11 层编译生成、非训练得到的 Transformer，用 Gated DeltaNet 状态当 RAM，让 RISC-V 程序在前向计算里执行。 divagr18/qwenputer 值得琢磨的是这种分工：语言能力靠学习，精确执行靠编译，不必把所有能力都押在训练上。它仍是实验项目，执行会消耗 trace token，不等于免费获得计算能力。

Impressive. Very small scale tho. This is what Juwel is - weve built the systems of systems inside of the model directly. divagr18/qwenputer

@grattonecon Funny but on a more serious note there are now techniques to emulate an entire von Neuman style computer inside an LLM: divagr18/qwenputer

Git hub repo : divagr18/qwenputer • RISC-V CPU inside a 4B model • Gated DeltaNet layers acting as RAM • Runs C and MicroPython internally • No external runtime needed One of the wildest thing I saw in the beginning of Q4 2026.

I ran Doom E1M1 on a Qwenputer-derived neural CPU I got a real 320×200 Doom E1M1 gameplay frame from a standalone, fixed-weight neural CPU. The run generated 28,699,242 autonomous protocol tokens and took about 2 hours 21 minutes end-to-end. Its cumulative native Metal inference rate was 4,193.5 tokens/sec. The earlier MLX backend measured 501.5 warm tokens/sec. These are different backends and measurement scopes—not a controlled head-to-head comparison. The framebuffer was extracted from neural memory and independently verified against the expected raw pixel hash. A scope clarification: this follows Qwenputer’s core idea of compiling CPU logic into neural weights and recurrent state, but it is a separate standalone Qwen-derived checkpoint—not stock Qwen and not the same model as upstream Qwenputer. Doom’s guest program and data initialize the machine; the neural circuit autonomously executes it. This is not real-time gameplay, and 10,000 TPS remains unmet. Project and evidence: X: x.com Github: Yashwanth-Chandrakumar/qwenputer-doom Qwenputer: divagr18/qwenputer

I ran Doom E1M1 on a Qwenputer-derived neural CPU I got a real 320×200 Doom E1M1 gameplay frame from a standalone, fixed-weight neural CPU. The run generated 28,699,242 autonomous protocol tokens and took about 2 hours 21 minutes end-to-end. Its cumulative native Metal inference rate was 4,193.5 tokens/sec. The earlier MLX backend measured 501.5 warm tokens/sec. These are different backends and measurement scopes—not a controlled head-to-head comparison. The framebuffer was extracted from neural memory and independently verified against the expected raw pixel hash. A scope clarification: this follows Qwenputer’s core idea of compiling CPU logic into neural weights and recurrent state, but it is a separate standalone Qwen-derived checkpoint—not stock Qwen and not the same model as upstream Qwenputer. Doom’s guest program and data initialize the machine; the neural circuit autonomously executes it. This is not real-time gameplay, and 10,000 TPS remains unmet. Project and evidence: X: x.com Github: Yashwanth-Chandrakumar/qwenputer-doom Qwenputer: divagr18/qwenputer Edit: I’d really appreciate your help sharing this with other communities so more people can learn about it. I’m unable to post in communities like r/LocalLLaMA and r/singularity because of their posting rules. If you’re active there and think it’s relevant, would you consider sharing it—while following each community’s rules? Thanks!

OK wtf. Somebody has twiddled a low-parameter Qwen #LLM to contain an entire #riscv CPU in the weights, no tool calls. divagr18/qwenputer

有人把一台 RISC-V CPU 直接编译进了 Qwen3.5 4B 的权重里。 Qwenputer 不靠训练，直接设权重，加 11 层，拿模型自带的 Gated DeltaNet 层当内存。算术在前向计算里完成，不调外部工具，作者称长整数运算从 48.6% 到 100%。 24G 显卡能跑，已开源：divagr18/qwenputer

A RISC-V CPU compiled into the weights of an LLM