divagr18

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

gpu-instance-fingerprint: Fingerprint the exact GPU you're running on, even among identical GPUs, from the order its atomic adds land in. 99.1% accuracy, survives reboots, can't be faked, and a default torch.index_add_ call leaks it.

Fingerprinting GPUs. divagr18/gpu-instance-fingerprint t.co

🤯模型里又跑起了另一个模型 上次是权重里塞 RISC-V。这次同一作者把 NPU 编译进 Qwen3.5-4B，再跑 Karpathy 的 stories260K。视频里，里面的小模型在写故事。 温馨提示：实验演示，不是日常速度。你会先看完视频吗？ 来源：QwenPU👇 divagr18/QwenPU t.co

An LLM inside an LLM - Same guy who put a RISK-V CPU inside Qwen has now made Karpathy's stories260k run on an NPU compiled inside Qwen 3.5-4B's weights Code: divagr18/QwenPU Weights: huggingface.co From the original tweet: 1/3 A CPU inside an LLM is one thing, but running another LLM inside an LLM would be taking things a bit too far, wouldn't it? Anyways, here's an LLM running inside an LLM : QwenPU, an LLM (@karpathy's stories260k) running on an NPU compiled inside Qwen 3.5-4B's weights. 2/3 stories260K is just a C program. To run it inside Qwen, I compiled an NPU into Qwen's weights that does matmuls, softmax and attention bit-exact. The LLM weights are in Qwen's own memory, with no external tools. A lot of work went into making it fast, more on that soon. 3/3 I'm super sick right now, so the full writeup is still a work in progress, and I'm working on optimisations and size reductions too. Stay tuned, I'll finish the blog & post it soon.

@karpathy 3/3 I'm super sick right now, so the full writeup is still a work in progress, and I'm working on optimisations and size reductions too. Stay tuned, I'll finish the blog & post it soon. Code and weights: divagr18/QwenPU huggingface.co modelscope.ai

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 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

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