The Open Source Initiative released its Open Source AI Definition 1.0 on October 28, 2024, setting four freedoms for AI systems: to use, study, modify and share them without needing permission, as InfoWorld reported. That definition matters for anyone choosing open source AI models, because many models called open do not meet it.
This guide explains the difference between open weights and open source, how licences differ, what hardware you need, and a five step method to pick a model. It draws on InfoWorld’s report on the OSI definition and Layer3Labs’ open source LLM guide, updated October 4, 2026.
Open Source or Open Weights: What the Label Means
Many free AI models share their weights, the trained parameters, but not their training data or full code. The OSI definition goes further: it asks for the data information, code and parameters needed to study and modify a system. InfoWorld notes critics say a model can still withhold training data and that the definition does not address copyrighted content in training data.
For a beginner, the practical lesson is simple. A model labelled open can have very different rights attached. Check the licence before you build anything on it.
Licences at a Glance
Layer3Labs summarises the main licence families. Confirm the current terms on each model card, since licences can change.
| Licence type | Models mentioned (Layer3Labs) | What it means in practice |
|---|---|---|
| Apache 2.0 | Qwen, Mistral, OLMo, Falcon | Permissive, broad commercial use |
| MIT | Some DeepSeek and GLM models | Permissive, few conditions |
| Custom terms | Gemma | Read the specific conditions |
| Community licence with caps | Llama | Limits apply, such as usage caps |
You may also see searches for OpenAI open source models. Whatever the provider, apply the same test: read the licence and model card, and do not rely on a headline.
What Hardware Do You Need
Layer3Labs says Gemma and small Mistral and Qwen models suit a laptop or a single GPU. Larger models need more memory and often a dedicated graphics card or a cloud server.
| Your setup | Sensible starting point |
|---|---|
| Everyday laptop | A small model, run through Ollama or LM Studio |
| Desktop with a single GPU | A small to mid-sized model of your choice |
| No suitable hardware | A hosted service or cloud server, with attention to data privacy |
Our guide to what edge AI is explains why running models close to the user is gaining ground.
Tools That Run Models Locally
Layer3Labs names Ollama and LM Studio as ways to run models locally. Both let a beginner download a model and chat with it without writing much code.
Start with one tool, one small model and one simple task, such as summarising your own notes. Once that works, compare a second model on the same task.
A Five Step Method to Pick a Model
Layer3Labs’ method boils down to five steps. Use it as a checklist.
| Step | What to do |
|---|---|
| 1. Define the task | Chat, summaries, coding, or a specific domain |
| 2. Check your hardware | Match model size to your memory and GPU |
| 3. Check the licence | Confirm commercial use and any caps |
| 4. Consider reproducibility | For regulated work, consider fully open models such as OLMo |
| 5. Pilot two models | Test both on your own real examples |
If coding is your main task, see our roundup of the best AI coding tools in 2026.
Safety and Quality Checks Before You Rely on a Model
Open models still make mistakes and can invent facts. Our explainer on AI hallucinations shows how to spot them, and the technique called retrieval is covered in our piece on the RAG fix for hallucinations.
Keep personal and confidential data out of any model until you understand where it runs and what is logged. A local model keeps data on your machine, which is a real advantage, but only if the tool is set up that way.
