Understand local AI before you spend a euro
Straight answers to the hardware questions everyone asks: how much VRAM you need, what quantization changes, whether a CPU is enough, and how to pick a GPU that will still be useful in two years.
How to Run an LLM Locally: The Complete Beginner Path
From choosing a model to your first local chat in under 15 minutes — Ollama, LM Studio and llama.cpp paths for any hardware.
Read guideHow to Choose a GPU for Local AI: A Buyer's Framework
A decision framework for buying a GPU for local LLMs — VRAM first, bandwidth second, vendor stack third, with specific card recommendations by budget.
Read guideLaptop vs Desktop GPUs for Local AI: What You Give Up
How laptop GPUs differ for local AI — power limits, thermals and bandwidth — plus honest guidance on what works on a gaming laptop.
Read guideCan I Run an LLM Without a GPU? Yes — Here's What Works
What CPU-only local AI can realistically do by RAM size and model class, with honest speed expectations and setup paths.
Read guideHow Context Length Affects Memory in Local LLMs
Why the KV cache grows with context, how many GB a long context really costs, and how to plan VRAM for long-document work.
Read guideTokens Per Second Explained: What Speed Do You Actually Need?
What tok/s measures, why it varies so much between machines, and realistic targets for chat, coding and reasoning workloads.
Read guideCUDA vs ROCm: Choosing the Right Stack for Your GPU
How NVIDIA's CUDA and AMD's ROCm differ for local AI in maturity, tooling and performance — and which runtime to pick on each.
Read guideHow Much VRAM Do I Need for Local LLMs?
A practical VRAM guide by model size, quantization and context — from 4 GB laptops to 24 GB desktops — with the math behind the numbers.
Read guideCPU vs GPU for LLM Inference: What Actually Determines Speed
Why token generation is memory-bandwidth-bound, when a CPU-only setup is genuinely usable, and how to compare your options.
Read guideWhat Is GGUF? The File Format Behind Most Local Models
How GGUF packs quantized weights into a single portable file, what the Q-labels mean, and why it became the default for local AI.
Read guide4-bit vs 8-bit Quantization: Which Should You Use?
How the two most common quantization levels differ in memory, speed and quality — and a decision rule that works on any hardware.
Read guideWhat Is Quantization? The Trade-Off Behind Every Local Model
Why 4-bit models fit where 16-bit ones don't, what quality you trade, and how to choose the right quantization for your hardware.
Read guideVRAM vs RAM for Local AI: Which Memory Actually Matters?
Where model weights live, when system RAM helps, and why dedicated GPU memory is usually the first wall local AI hits.
Read guideSkip the reading and just ask
The hardware checker turns this whole guide library into one personalized answer for your machine.