Guides, comparisons, and tips for running AI models locally on your own hardware.
Which AI models can your Mac actually run? A practical guide to local LLMs on Apple Silicon — from Mac Mini M4 Pro to Mac Studio M4 Ultra, with specific model recommendations for each memory config.
Head-to-head comparison of Google's Gemma 3 and Meta's Llama 3 for local inference. Compare VRAM requirements, performance, and quality at each size tier to pick the right model for your GPU.
Complete VRAM requirements guide for all DeepSeek R1 model sizes. Find out exactly how much memory you need to run R1 locally with Ollama at different quantization levels.
Everything you need to know about running Qwen 2.5 models locally. Compare Qwen 2.5 base vs Coder variants, VRAM requirements for each size, and which quantization to use with Ollama.
GLM-5.1, Kimi K2.5, Nemotron Ultra, Granite 3.3, and SmolLM2 are the latest additions to Ollama. Here's what each one does, how much VRAM they need, and which hardware can run them.
A practical breakdown of which AI models fit at each VRAM tier. From budget 8 GB cards to the RTX 5090, find the best Ollama models for your exact amount of VRAM.
Google's Gemma 4 ships four model sizes under Apache 2.0 — from 2B edge models to a 31B dense powerhouse that rivals 400B+ rivals. Here's what it means for local AI.
An honest look at the best GPUs for local LLM inference in 2026 — covering the new RTX 50-series, the used market bargains, and why a 3-year-old card might still be your best bet.
How to set up a self-hosted AI chat server using a Mac Mini for inference and a Raspberry Pi for the web interface — accessible from anywhere, running for about $1/month.
Step-by-step guide to running Meta's Llama models on your own hardware with Ollama — covering Llama 3.x, Llama 4 Scout, and how to pick the right model for your GPU.
A plain-English explanation of AI model quantization — what Q4_K_M, Q8_0, and the newer formats actually mean, and how to pick the right one for your hardware.