Raspberry Pi is a line of small single-board computers used by hobbyists and makers, including for running local models and DIY devices. Recent coverage includes Meta open-sourcing code to run its Muse AI agent on ESP32 boards or a Raspberry Pi; a developer reporting that a quantized Qwen-3.8-27B run through a Pi agent handled complex refactors at about 2.4 cents per 1M input tokens and 70 cents per 1M output, though Pi's edit tool was the weak link; and advice against FOMO-driven spending on local LLM hardware.
Meta open sources code for DIY Muse AI gadgetsMeta has open sourced code letting users build their own devices running its new Muse AI agent on off-the-shelf ESP32 boards or Raspberry Pi, connecting displays, buttons and sensors for DIY projects like reminders, big-screen display or touchscreen interaction.
User says Qwen-3.8-27B good enough to drop APIA developer said Qwen-3.8-27B, quantized to Q4_K_S with Q8_0 context, completes complex refactors on its own, so they stopped using an API. Running via a Pi agent on a Raspberry Pi, cost is about 2.4 cents per 1M input tokens and 70 cents per 1M output. The author noted Pi's edit tool is the weak link.
Advice for hobbyists: don’t let FOMO drive local LLM spendingAdvice to hobbyists: don’t let FOMO drive costly local LLM setups—learn via APIs or small models that fit existing hardware; you can gain more by doing less rather than buying expensive GPUs.
The A.I. Daily