I ran Qwen3.5-0.8B on a sub-$20 CPU chip in under 512MiB of memory

4/5 on ARC-Easy. This is a 5-case smoke test with greedy decoding, scoring rule fixed before the run. Not the official benchmark. The chip is an Amlogic A113X: quad Cortex-A53 from 2017, $10-20 class, no NPU, no GPU. It's inside a ThirdReality smart home hub with 2GB RAM. I wrote a custom C runtime for it. Single static binary, no Python, no llama.cpp. Measured on-device: Prefill: 2.92 tok/s Steady decode: 1.82 tok/s Peak RSS: 490 MiB, zero swap CPU: ~334% of 4 cores The wrong one was "Which technology was developed most recently?" — it picked television, answer is cellular telephone. I think this is acceptable for a 0.8B model. Why do this: memory is expensive. If inference fits in 490 MiB, it can run on hardware already deployed in the field — smart home hubs, gateways, 1GB boards that no vendor stack supports. The method is to compile one pinned model for one pinned CPU target ahead of time, instead of a general runtime that accepts anything at load. Decode throughput is bounded by memory bandwidth divided by bytes per token, and a GPU is not part of that equation. This run is actually still compute-bound after a 4.42x kernel speedup, so there is headroom left. For comparison, the same model with a generic C runtime on an M3 Pro decodes at 2.39 tok/s. The A113X is within 25% of that. My take: a lot of inference doesn't need a GPU. Maybe the world already changed and Nvidia has no reason to point it out. Full report with exact prompts, outputs, timings, and hashes: https://github.com/baryhuang/cpu-llms-in-c/tree/main/models/qwen3.5-0.8b/benchmarks/arc-easy-5 Repo: https://github.com/baryhuang/cpu-llms-in-c/tree/main/models/qwen3.5-0.8b I'm learning and looking for collaborators. I will help you build a model for your CPU.