AI glossary
Open Weights
Open weights describes a model whose trained parameters (weights) are published and can be downloaded and run by anyone, as distinguished from a fully closed model, where access is provided only through an API to run inference, without the underlying weights ever being released.
Consider the difference between renting a house and buying the blueprints. With a closed model, you are essentially renting the house: you can live in it (use the API), but you cannot tear down walls, move plumbing, or see exactly how the foundation was poured. With an open weights model, you get the blueprints and the structure. You own the trained parameters, meaning you can inspect the architecture, run the model on your own hardware, and modify it to fit your specific needs. This distinction is critical for developers who need transparency and control over their AI infrastructure.
Open weights vs open source vs closed models
To understand where open weights fits in the AI landscape, it helps to compare it directly with the two other main categories: closed and fully open-source models.
A closed model gives users access only through an API. You send input and receive output, but the model’s weights, training code, and training data all remain private to the provider. You cannot see how the model works internally, and you cannot modify its behavior directly.
An open-weights model publishes the trained weights themselves. This allows anyone to download the model and run it on their own hardware, inspect its architecture, or fine-tune it further. However, this does not necessarily mean the training code or training data used to create it is also released.
A fully open-source model would additionally release the training code and training data used to produce it, not just the resulting weights. Open-weights models often do not disclose their full training data or the complete code used to train them. This is why “open weights” and “open source” are treated as distinct terms rather than synonyms. While open source implies full reproducibility, open weights implies accessibility and modifiability of the final product.
Licensing
Open-weights models are typically released under a specific license defining what uses are permitted. These licenses vary by model and are not automatically equivalent to standard open-source software licenses such as MIT or Apache 2.0.
Some open-weights licenses include restrictions, such as limiting commercial use above a certain scale or restricting use by very large companies. This means the exact permissions must be checked for each specific model and license rather than assumed from the “open weights” label alone. For example, a model might be free for personal use and small businesses but require a paid enterprise agreement for large-scale commercial deployment. Always read the specific Foundation model license to understand these constraints before integrating the model into your production pipeline.
Examples
Several major players have adopted the open weights strategy, giving developers and data scientists a range of options to choose from.
- Meta’s Llama model series: These models are widely used for their balance of performance and accessibility. They allow extensive fine-tuning and community development.
- Mistral AI’s models: Known for efficiency and strong performance in smaller parameter sizes, these models are popular for edge deployment and specialized tasks.
- Google’s Gemma model family: Google provides these models with clear licensing, often emphasizing research and commercial flexibility.
- Alibaba’s Qwen model series: These models offer strong multilingual capabilities and are increasingly popular in global markets.
These Large language model options demonstrate the diversity available in the open weights space. Whether you need a Small language model for edge devices or a larger variant for complex reasoning, the open weights ecosystem provides viable alternatives to proprietary APIs.
Why it matters
Open weights let a model be run locally rather than only through a provider’s API. This can matter for data privacy, latency, offline use, and avoiding ongoing per-request API costs. When you host the model yourself, you control the data flow, which is crucial for industries with strict regulatory requirements like healthcare or finance.
They also allow the model to be fine-tuned or otherwise modified by the downloader for a specific task. A fully closed, API-only model does not allow this, since its weights are never made available for direct modification. By accessing the Model parameter set, you can adapt a general-purpose model to your niche domain, improving accuracy and reducing inference costs over time. This process is commonly known as Fine-tuning, and it is one of the primary reasons developers choose open weights models.
FAQ
What does open weights mean?
Open weights means that the trained parameters of a model are published and downloadable. You can run, inspect, and modify the model, unlike with closed models where only the API is accessible.
Is open weights the same as open source?
No. Open source includes the training code and data along with the weights. Open weights only releases the trained model weights, not necessarily the code or data used to create them.
Can I use open weights models commercially?
It depends on the specific license. Some open-weights licenses allow commercial use, while others restrict it based on user scale or revenue. Always check the license for each model.
Why should I choose an open weights model over a closed API?
Open weights models offer better data privacy, lower latency, offline capability, and the ability to fine-tune the model for specific tasks without relying on third-party providers.