danveloper/flash-pi-dsv4

DeepSeek V4 Flash is a large AI language model. This project is a specialized C program that makes it possible to run that model directly on a Raspberry Pi 5 (a credit-card-sized single-board computer) using a fast NVMe solid-state drive rather than the slow microSD card most Pi projects use. The project's goal is narrow: get as many generated words per second as possible from the Pi's CPU, with no cloud server or Mac in the path. The result is roughly one token (one word fragment) per second, which is slow by server standards but usable for interactive sessions on a low-cost device. The model involved is 284 billion parameters and uses a mixture-of-experts architecture, meaning it contains many specialized sub-networks and only a small fraction need to run for each token generated. The project takes advantage of this by reading only the relevant expert weights from NVMe storage when needed, letting Linux's built-in file caching act as the expert data cache, and packing the weights in a layout that makes those reads more predictable. Several other optimizations are applied: the model is quantized (compressed to use fewer bits per number), frequently used weight matrices are stored in a precomputed cache, and a small Python script wraps the process to stop generation when the model goes off track after a reasoning-mode marker. Setup requires a Raspberry Pi 5 with 8 GB RAM, active cooling, and an NVMe SSD of 256 GB or larger. The steps involve cloning the repo, downloading the model file from Hugging Face, building the C engine on the Pi, and generating a precomputed expert weight pack. The README describes this as experimental and not intended as a general-purpose AI inference tool.