Getting Started with Local LLMs

A beginner's guide to running language models on your computer

Local LLMs – Start without Cloud in a Few Steps

Getting started with local LLMs is easier than many think. You don’t need a cloud API, no monthly costs, and your data stays entirely on your own hardware. This guide shows you how to get first results quickly – and what you actually need.

Why Go Local?

Local LLMs have clear advantages:

• Privacy & Control – all data stays on your machine
• No recurring costs – no token prices, no subscriptions
• Works offline – internet only needed for download
• Full flexibility – freely combine models, prompts, and tools

Of course there are limits: large models require powerful hardware and some technical understanding helps.

Hardware Requirements

Hardware is the most important factor for local LLMs. The larger the model, the more memory and computing power are needed.

Minimum (small models up to ~8B)

• Modern CPU (Apple Silicon or current x86 CPUs)
• 16–32 GB RAM
• SSD storage (models are several GB in size)

Recommended (medium models 8–20B)

• Dedicated GPU with 8–16 GB VRAM
• 32 GB+ RAM
• NVMe SSD for fast loading times

High-End (large models 30B+)

• GPU with 24 GB+ VRAM (RTX 3090/4090 or equivalent)
• 64 GB RAM or more
• Good cooling and stable power supply

Tip: You can also start without a GPU, but CPU inference is significantly slower.

Getting Started

1) Choose an Inference Engine

An inference engine is the software that runs the model. Popular options:

• Ollama – minimalistic, fast, CLI-based
• LM Studio – graphical interface, ideal for desktop users
• GPT4All / Jan – simple local chat UIs

For beginners, a GUI solution is more pleasant; advanced users often prefer the CLI.

2) Download a Model

Models are usually loaded directly through the engine. Pay attention to model size:

• Small models (7–8B): run on most computers
• Medium models (15–20B): GPU recommended
• Large models (30B+): high-end hardware or strong quantization needed

Common local models:

• LLaMA variants
• Mistral
• DeepSeek
• Gemma
• Qwen

3) Generate Text

Once the engine and model are installed, you can start immediately.

Example with Ollama:

ollama run gemma3

From now on, your model answers prompts completely locally – without an internet connection.

Common Pitfalls

• Performance: CPU-only is slow – GPU pays off
• Storage: Models can take up many GB
• Thermals: sustained load requires good cooling
• Tool differences: Not every UI supports every feature

Conclusion

Local LLMs are no longer an experiment but a practical workflow. With modern standard hardware, you can run your first model locally in less than an hour – secure, independent, and without recurring costs.

The barrier to entry is low, the possibilities are enormous.