Why Open Source AI Is Good for Developers
When I talk to developers, CEOs, and government officials across the world, I usually hear several themes:
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When I talk to developers, CEOs, and government officials across the world, I usually hear several themes:
We need to train, fine-tune, and distill our own models.
Every organization has different needs that are best met with models of different sizes that are trained or fine-tuned with their specific data. On-device tasks and classification tasks require small models, while more complicated tasks require larger models. Now you’ll be able to take the most advanced Llama models, continue training them with your own data and then distill them down to a model of your optimal size – without us or anyone else seeing your data.
We need to control our own destiny and not get locked into a closed vendor.
Many organizations don’t want to depend on models they cannot run and control themselves. They don’t want closed model providers to be able to change their model, alter their terms of use, or even stop serving them entirely. They also don’t want to get locked into a single cloud that has exclusive rights to a model. Open source enables a broad ecosystem of companies with compatible toolchains that you can move between easily.
We need to protect our data.
Many organizations handle sensitive data that they need to secure and can’t send to closed models over cloud APIs. Other organizations simply don’t trust the closed model providers with their data. Open source addresses these issues by enabling you to run the models wherever you want. It is well-accepted that open source software tends to be more secure because it is developed more transparently.
We need a model that is efficient and affordable to run.
Developers can run inference on Llama 3.1 405B on their own infra at roughly 50% the cost of using closed models like GPT-4o, for both user-facing and offline inference tasks.
We want to invest in the ecosystem that’s going to be the standard for the long term.
Lots of people see that open source is advancing at a faster rate than closed models, and they want to build their systems on the architecture that will give them the greatest advantage long term.
Very interesting point here:
Third, a key difference between Meta and closed model providers is that selling access to AI models isn’t our business model. That means openly releasing Llama doesn’t undercut our revenue, sustainability, or ability to invest in research like it does for closed providers. (This is one reason several closed providers consistently lobby governments against open source.)