Guides

Plain explanations with numbers from the same engine as the configurator.

• Start here

  Local LLMs for beginners: everything explained simply

  Run an AI model on your own computer, without the jargon: memory, model size, quantization, speed, first steps and a glossary.

• How much VRAM do you need for a local LLM?

  The formula behind LLM memory — weights, KV cache, overhead — with the memory popular models need at each quantization.

• 12 GB vs 16 GB vs 24 GB of VRAM for local LLMs

  What each VRAM tier actually runs, from 8 GB to 32 GB, with the largest model that fits entirely on the GPU.

• Q4 vs Q5 vs Q8: which quantization for a local LLM?

  What GGUF quantizations change in memory and quality, and how to choose between Q4_K_M, Q5_K_M, Q6_K and Q8_0.

• Local LLMs on a Mac: how unified memory really works

  Why 64 GB of unified memory is not 64 GB of VRAM, how much the GPU can use, and which Macs run which models.

• CPU vs GPU for local LLMs: why memory bandwidth decides

  Token generation reads every weight once per token. Here is why GPUs are 10× faster than CPUs, and when a CPU is enough.

• Which GPU to buy for local LLMs: VRAM first

  How to choose a graphics card for local AI: VRAM sets the model size, bandwidth sets the speed. The largest model each popular GPU runs.

• Mac or PC for a local LLM? Memory vs speed

  A Mac holds bigger models in unified memory; a PC with an NVIDIA GPU generates faster when the model fits. Estimated speeds side by side.

• LM Studio step by step: run your first local LLM

  Install LM Studio, pick a model that fits your GPU or Mac, load it and chat — plus the local server. A beginner walkthrough.