Definition
Open weights and open source models
Models whose parameters are published, so anyone can run and fine-tune them in their own environment.
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Related terms in AI Fundamentals
On-Device AIModel inference that runs locally on your phone instead of sending data to a cloud server. Savva supports airplane mode verification so users can confirm queries never leave the device.Open sourceSoftware whose source code is freely available for anyone to view, use, modify, and share.Open source / open weightsModels you can download, run, and keep, with no provider able to revoke access.Open source modelsAn AI model whose weights, code, and details of its training data are released so anyone can run, inspect, modify, and build on it.Open-Source LLM StrategyThe decision to release AI model weights and architecture publicly, enabling community contribution, local deployment, trust verification, and developer adoption at scale. China's approach: all major LLMs released open source. US approach (except Meta): all major frontier models remain closed. Open source compounds via the scientific method — thousands of contributors find flaws, optimize, build smaller deployment-specific variants, and expand use cases. The strategic argument: whoever wins developer adoption wins the long-term market, and open source is how you build with developers rather than selling to them.Open-weight modelA model whose trained weights are publicly downloadable, even if its training data and code are not.Optimization algorithmThe method a model uses during training to adjust its parameters and make fewer mistakes, such as gradient descent.Opus-class vs Sonnet-classShorthand for top-tier versus the still-excellent, cheaper tier most workloads can live in.