Distillation for Smaller On-Device Models Local deployment is often constrained by physics more than ambition. Laptops, workstations, and edge devices have finite memory bandwidth, limited thermal headroom, and strict latency budgets. Distillation is one of the most important ways teams turn a large, capable model into a smaller model that behaves well enough to be […]

Fine-Tuning Locally with Constrained Compute Fine-tuning is often described as “make the model better for my domain.” In practice it is “change the model’s behavior under strict constraints.” Local tuning is especially constraint-driven: limited VRAM, limited time, limited ability to run large sweeps, and strong requirements around privacy and reproducibility. The teams that succeed locally […]

Private Retrieval Setups and Local Indexing Retrieval is the difference between “a model that can talk” and “a system that can work.” When you connect local models to private documents, the goal is not only better answers. The goal is answers that are grounded, traceable, and aligned with the boundaries that matter: personal privacy, organizational […]