In a significant shift towards open collaboration, Apple has made available several open source large language models (LLMs) dubbed OpenELM. Unlike the conventional reliance on cloud processing, these models are designed to run directly on devices, offering greater efficiency and privacy. These models, combined with an instructional framework and logs, are accessible on the Hugging Face Hub, fostering a community-driven approach in the AI space. Apple’s white paper details the unique layer-wise scaling strategies of OpenELM that enable better accuracy without the extensive resource demands typically associated with such AI models. The release of OpenELM by Apple is not simply about providing the AI community with powerful tools; it's about transparency and trust in AI development. By offering not just the pre-trained models but also the underlying data, logs, and training configurations, researchers and developers gain a comprehensive understanding of the models' workings. This approach is a departure from traditional practices that tend to keep datasets private and fosters a more communal and verifiable path to AI advancements. It reflects Apple's commitment to empowering the research community to explore and innovate while setting new benchmarks in AI model accuracy and efficiency. Apple’s strategy of sharing these AI resources does more than advance the state of AI research—it also serves as a compelling tool for attracting top-tier talent. By providing a framework where researchers can share results that, in Apple's typically confidential environment, may not have seen the public light, they can now contribute to a larger body of work. While these AI capabilities are novel, they signal a future where Apple devices will natively run such models, aligning with broader privacy objectives and expected AI integrations in upcoming iOS releases.