Making budget models punch above their weight with a smart Rust harness

- This post discusses a technique for running small open-source language models (e.g., the Llama family) more efficiently by writing a custom inference harness in Rust, rather than using standard tools like llama.cpp or Ollama. - "Punching above their weight" means making a budget (low-parameter-count) model perform comparably to a much larger one by optimizing the inference pipeline, quantisation, prompt formatting, and sampling parameters. - The author (Yogthos) is a prominent figure in the Clojure community and a vocal critic of "enshittification" in Big Tech; the post reflects a broader DIY/AI-sovereignty ethos — using open models and lean tooling to avoid cloud dependency. - Rust is chosen for its speed, memory safety, and low-level control, which matters when squeezing performance out of consumer-grade hardware (e.g., a single GPU or even CPU-only inference). - The piece assumes readers know what a transformer model, a tokeniser, KV-cache, and quantisation (e.g., 4-bit) are; it's aimed at developers who want to move beyond one-click solutions and understand the engineering underneath.