Easiest way to run LLMs locally (www.sitepoint.com)

This piece is a practical guide for getting started with locally hosted LLMs: why to do it, what you need, and the easiest tools to run them. Local models give you privacy, offline operation, lower ongoing cost, faster latency for many tasks, and the ability to fine-tune or serve models on your LAN. The rule of thumb: pick a model that fits comfortably in your RAM (with headroom for the OS) and ideally your GPU VRAM to avoid CPU offloading. Quantization (especially 4-bit Q4_K_M) is the key enabler—e.g., a 20B model that originally needs ~48 GB can be quantized down to ~14 GB while retaining much of its performance. On tooling, the article highlights llama.cpp as the inference backbone most wrappers use, and recommends Ollama for beginners (very simple GUI, limited configuration) and LMStudio for a richer, faster desktop experience (closed-source with minimal telemetry; you can firewall it). It also lists solid open-source alternatives—OpenWebUI, GPT4ALL, LocalAI, AnythingLLM, Koboldcpp—and model suggestions: GPT-oss, Gemma3, Qwen3, Phi4, Mistral 7B and task-specialized variants (Coder, Mathstral). Practical tips: prioritize GPU offload, prefer higher-parameter models within your hardware limits, and use the defaults most tools provide to avoid manual tuning.