Open source software has some clear advantages over commercial options, especially since you can download it for free. This means anyone with the right setup can dive right into the code and start using it without delay.
When we talk about AI, being “open” covers two main aspects. First, you can download the source code of the AI engine from a repository, run it on compatible hardware, and tweak it to your liking. Second, there’s the data model aspect. You can run a locally trained AI model without needing to connect to the internet, which helps protect against data leaks common with cloud-based services. Plus, without the ongoing costs of cloud-hosted models—where fees usually depend on how many tokens you use—you save on expenses.
So, what sets open models apart from commercial AI? All software needs a license. Commercial products often use subscription models where costs rise with usage, especially with large language models (LLMs) that charge based on the number of tokens processed and the GPU hours consumed. Open-source models fall under their own licensing terms, but you can typically run them locally without hefty fees. However, if you choose to run an open model in the cloud, you’ll still incur costs based on token usage.
The benefits of open-source AI models don’t stop at cost savings. Their shared nature promotes collaboration, with the community able to improve and debug code. This peer review can enhance model quality and address biases that arise from narrow training data.
Getting started with open models is relatively straightforward. Many provide free or low-cost web access for hands-on experimentation. API access often comes with usage fees based on token counts, including input and output measures. If you prefer to download models, you can find them on repositories like GitHub, which typically support various operating systems.
For those new to this, platforms like Hugging Face take the hassle out of getting started. They allow users to explore and test different models without needing to wrestle with technical setups. They offer both free and subscription options, catering to different user needs. Similarly, Ollama provides a command-line tool for easily running LLMs, while a graphical interface can be accessed through projects like Open WebUI.
Open-source AI models also bolster corporate IT security. Concerns have been raised about data leaks, particularly when employees use popular LLMs like ChatGPT without safeguards. High-profile incidents, like Samsung’s accidental exposure of sensitive code, highlight these risks. Although software providers claim to keep customer data separate from AI training, ensuring up-to-date and effective model training requires constant input from current practices.
An open model can be safely executed in a secure environment, whether onsite or in the cloud. While it represents a snapshot of the developer’s work, it allows organizations to keep data secure and retrain the model to align with their unique requirements.
In terms of hardware, running models like the Chinese DeepSeek-R1 can even be done on compact devices like the Nvidia Jetson Nano or Raspberry Pi, given they have the right GPU support. The more powerful the GPU and the more memory it has, the better performance you’ll see in response times. However, training models necessitates significant resources, making on-premise setups expensive. While cloud resources can alleviate some costs, they still require careful budgeting.
As the name suggests, large language models require substantial resources—both in data and computing power. Even open-source models entail heavy hardware investments, discouraging many from leveraging them fully. That’s where smaller language models (SLMs) gain traction. They’re less demanding on resources and can even run on everyday devices like smartphones and personal computers, making them a more accessible choice for a broader range of users.