What is an open source AI model?
An open source AI model is a model you can download and run yourself, with permission to use, inspect, modify and share it.
With a closed model like the ones behind ChatGPT or Gemini, you only get access through an app or API. The model's parameters stay on the company's servers. With an open model, the weights file itself is published, often on Hugging Face, so you can run it on your own laptop, server or cloud account.
The Open Source Initiative published an Open Source AI Definition in 2024. In short, it says a truly open source AI system should give access to the model parameters, the code used to train and run it, and enough detail about the training data for someone skilled to build a similar system.
Open source vs open weight vs closed models: what is the difference?
Open source models share weights, code and training details under a permissive licence, open-weight models share mainly the weights under their own licence, and closed models share none of these.
| Point | Open source | Open weight | Closed |
|---|---|---|---|
| Weights downloadable | Yes | Yes | No |
| Training code and data details | Shared | Usually limited | Not shared |
| Licence | Standard open licence | Custom licence, may have limits | Terms of service only |
| Run offline on your own machine | Yes | Yes | No |
| Fine-tune it yourself | Yes | Yes, within licence terms | Only via provider tools, if offered |
| Typical access | Download | Download | App or API |
Most famous "open" LLMs are really open-weight. Meta's Llama family, for example, ships downloadable weights under Meta's own Llama licence rather than a standard open source licence. The everyday phrase "open source AI model" usually covers both groups, so read the licence before you assume anything.
Why does the licence of an open AI model matter?
The licence decides what you are legally allowed to do with the model, including commercial use, redistribution and building products on top of it.
Some models use permissive licences such as Apache 2.0, which Mistral AI used for releases like Mistral 7B and Mixtral 8x7B. Others use custom licences that add rules, such as an acceptable use policy or extra conditions for very large companies, which Meta's Llama licences include.
For a college project, almost any open model is fine. For a startup or a client project, check three things: whether commercial use is allowed, whether you must show attribution, and whether outputs or fine-tuned versions carry restrictions. Licence terms can change between versions, so check the exact version you download.
What are the pros and cons of open source AI models?
Open models give you control, privacy and lower long-term cost, while closed models usually lead on raw capability and convenience.
Pros
- Data can stay on your own machines, which helps privacy and compliance
- No per-token bill once you run it on your own hardware
- You can fine-tune it on your own data, such as Hindi or Tamil customer chats
- Results do not change suddenly because a provider updated a model
Cons
- You need hardware, usually a good GPU for larger models
- You handle setup, updates, security and guardrails yourself
- The very strongest models are often closed, though the gap has narrowed
- Fewer ready-made features like web search or file uploads
I suggest beginners try both: use a closed chatbot for daily work, and run one small open model locally to understand what happens under the hood.
How can you run an open source AI model locally?
You can run an open source AI model locally by downloading a small, quantized version and loading it with a desktop tool or a Python library.
- Pick a small model, around 1B to 8B parameters, in a 4-bit version.
- Use a beginner-friendly local runner app, or the Hugging Face Transformers library if you know Python.
- Start with short prompts and watch your RAM and speed.
- Move to bigger models only if your hardware handles the small one comfortably.
A laptop with 16 GB of RAM can usually run a 4-bit 7B or 8B model, slowly on CPU and faster with a decent GPU. Every reply you get is local inference, with no internet needed.
Why do open source AI models matter in India?
Open source AI models matter in India because they help with data residency, cost control and support for Indian languages.
Banks, hospitals and government projects often prefer to keep sensitive data on servers they control inside India, and open models make that possible. India's Digital Personal Data Protection Act, 2023 has also made companies more careful about where personal data goes.
Cost is the other big reason. Paying per token in dollars adds up quickly for a high-volume app, while a well-chosen open model on rented GPUs can be cheaper at scale. Indian teams also fine-tune open models for Hindi, Bengali, Marathi and other languages that global models handle less well. Learning to work with open models is a useful skill for generative AI interviews.