Choosing a Mac for running local LLMs comes down to one number above everything else: unified memory. Unlike a Windows PC where the GPU has its own VRAM and the RAM is separate, Apple Silicon shares one memory pool between CPU and GPU. This means every gigabyte of RAM you buy is available to your … Read more
Continue is an open-source VS Code extension that connects your editor to any LLM — including local models running via Ollama — to give you AI-assisted coding that never sends your code to an external server. Where Cursor and GitHub Copilot route everything through cloud APIs, Continue lets you use Qwen2.5-Coder, Phi-4-mini, or any Ollama … Read more
Open WebUI is the most popular graphical interface for Ollama — it gives you a polished browser-based chat interface, conversation history, model management, and multi-user support, all running on your own machine. Where Ollama handles model downloading and inference through the command line, Open WebUI puts a clean UI on top of it. This guide … Read more
GGUF and ONNX are the two most widely used model file formats for deploying language models outside of training infrastructure, but they serve different purposes and different deployment contexts. Choosing between them is not a quality decision — both can represent the same model at the same precision — it is a deployment decision that … Read more
Small language models and large language models are not competing products — they are different tools suited to different jobs, and choosing between them requires understanding which properties of a model actually matter for the task at hand. The question is not “which is better” but “which fits the constraints and requirements of this specific … Read more
NVIDIA’s Jetson Nano is an embedded computing platform built specifically for AI inference at the edge. With its 4GB RAM, 128-core Maxwell GPU, and power consumption under 10 watts, it sits at an interesting intersection: capable enough for meaningful LLM inference but constrained enough that model choice and optimisation matter significantly. Running an LLM on … Read more
llm.js (also referred to as web-llm or browser-llm in some contexts) refers to a family of JavaScript libraries that enable language model inference directly in the browser or in Node.js environments without a Python backend. The space is fragmented — several projects use similar names — but the practical goal is the same: bring LLM … Read more
WebLLM is an open-source project from the MLC (Machine Learning Compilation) team at CMU that runs large language models directly inside the web browser using WebGPU. No server, no API key, no backend — the model weights download to the user’s machine and inference runs entirely in the browser tab using the GPU through the … Read more
On-device LLM inference means running the full model inference pipeline on the hardware where the application runs — no cloud API, no server, no network dependency. For developers building desktop applications, mobile apps, embedded systems, and edge deployments, understanding the full stack of on-device inference — model formats, runtime engines, hardware acceleration, and integration patterns … Read more
Running a language model directly on a smartphone — no server, no API, no internet connection required — was a niche experiment in 2023 and is a practical deployment option in 2026. The hardware caught up: modern high-end phones have 8–12GB of RAM, powerful NPUs, and the inference frameworks to use them efficiently. This guide … Read more
