Running a local Large Language Model (LLM) offers unparalleled privacy and control. Whether you are building web applications, analyzing complex datasets, or simply want to run a smart assistant without cloud dependency, the first step is choosing the right model. This guide helps you find the balance between functionality, hardware requirements, and speed.

1. Determine your main goal

Not every model excels at everything. Open-weights models are often fine-tuned for specific tasks. It is crucial to choose a model that aligns with your workflow.

Coding and Web Applications

For writing code, debugging scripts, or generating frontend frameworks, you need a model trained on large amounts of programming language. Models from the DeepSeek Coder or Qwen-Coder families are currently the absolute top for local development. They understand complex logic and integrate seamlessly into programming environments.

RAG and Summarization

When building an application based on Retrieval-Augmented Generation (RAG) with vector search, the context window (the maximum number of tokens the model can remember at once) is vital. Models like Mistral NeMo or specific Llama 3.1 variants offer generous context lengths that are perfect for parsing and summarizing long documents.

General Chatting

For daily interactions and creative brainstorming sessions, you want an instruction-tuned model. The standard Llama 3 instruct models or Phi-3 offer an excellent balance between conversational quality and response speed.

2. Model Size versus Speed

The size of a model is expressed in parameters (often denoted with a 'B' for billion). Your hardware determines the upper limit of what you can run smoothly.

Less than 10B (e.g., 7B, 8B): Fast and efficient. Ideal for standard laptops (8GB to 16GB RAM). They respond instantly but sometimes lack the deep logic for highly complex tasks.
Between 14B and 35B: The "sweet spot" for serious developers. Typically requires 16GB to 32GB of shared memory or a powerful graphics card. They offer excellent logic and coding capabilities.
70B and larger: This requires specialized hardware. If you have (or plan to build) a home HPC setup with multiple powerful GPUs, these models offer performance that rivals premium commercial cloud models.

Tip: Pay attention to quantization (such as GGUF) when downloading. A 4-bit or 5-bit quantized model significantly reduces the required memory, with only a minimal loss of intelligence.

3. Multilingualism: Dutch Support

If your local AI needs to understand and generate flawless Dutch, be selective. Many smaller models quickly fall back on a kind of 'broken English' with Dutch grammar.

For specific Dutch tasks, the GEITje model (based on Mistral) is a fantastic local alternative. For larger models, the rule is: the more parameters, the better the multilingual support. A 70B Llama 3 or Qwen model speaks fluent Dutch, simply due to the immense volume of training data.

Exploring further

The landscape of local models changes weekly. It is recommended to start small, test the speed on your own system, and discover which model integrates best into your projects.

Want an overview of the latest and most popular models that you can download directly? Then check out our complete overview in the local LLM directory.