# Running an LLM locally on your own machine | LLM Guide [Skip to content](#lm-inhoud)Network/[NL](/en/llm-lokaal-draaien)EN[Hubhub.llmnet.nlCompare models on task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organization, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien&text=Running%20an%20LLM%20locally%20on%20your%20own%20machine)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien)[](https://www.reddit.com/submit?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien&title=Running%20an%20LLM%20locally%20on%20your%20own%20machine)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien&text=Running%20an%20LLM%20locally%20on%20your%20own%20machine)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien)[](https://www.reddit.com/submit?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fllm-lokaal-draaien&title=Running%20an%20LLM%20locally%20on%20your%20own%20machine)[](#)By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 27, 2026 # Running an LLM locally on your own machine A practical guide for maximum privacy, control, and speed. Using artificial intelligence doesn't always have to happen via the cloud. More and more users are choosing to run a Large Language Model (LLM) locally on their own computer. This might sound complex, but with modern tools, it is now as easy as installing a web browser. ## Why run an LLM locally? The main reason is privacy. When you run a model locally, not a single prompt, document, or line of code leaves your computer. Your data is not used to train models for big tech companies. Additionally, you are completely independent of an internet connection, won't suffer from server outages, and don't pay monthly subscription fees or API rates. ## General hardware requirements An LLM requires memory (RAM/VRAM) to function. Although the requirements depend on the size of the model, you can use the following general guidelines: - Memory (RAM/Unified Memory): Minimum of 8GB for small models, 16GB or more is highly recommended for a smooth experience. - Processor (CPU/GPU): A modern multi-core CPU is sufficient for basic use, but a powerful graphics card (NVIDIA GPU with enough VRAM) or an Apple Silicon Mac (M1/M2/M3) makes text generation much faster. ## The best tools right now You don't need any programming knowledge to get started. Two tools currently stand out from the rest: - LM Studio: The perfect choice for beginners. It offers a clean graphical interface that closely resembles well-known cloud chatbots. You can search for new models directly from the app and download them with a single click. - Ollama: A lightweight, lightning-fast tool that works primarily via the command line (terminal). It is a favorite among developers because it integrates seamlessly with countless other applications and web interfaces. ## Step-by-step guide: Your first local LLM (via LM Studio) ### Step 1: Download and install Go to the official LM Studio website and download the version suitable for your operating system (Windows, macOS, or Linux). Install the application just like you would install any other program. ### Step 2: Search for a model Open the application. Use the built-in search bar and type in a popular, efficient model, such as Llama 3 or Mistral. LM Studio will immediately show you whether a model is suitable for the amount of memory in your computer. ### Step 3: Download Click the download button next to the model. Make sure to choose a so-called 'Quantized' version (often indicated by Q4 or Q5); these are compressed and run much more smoothly on consumer hardware without much loss in quality. ### Step 4: Start chatting! Navigate to the chat icon in the left menu, select the model you just downloaded from the dropdown menu at the top, and start typing. You now have your own, completely private AI assistant! Want to learn more about the technical inner workings of language models, or learn how to integrate them into your own workflows? Take a look at our [educational hub](https://leren.llmnet.nl/en/) for in-depth articles and tutorials. © 2026 LLMnet.nl — Independent guide to open-source AI.