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Apple reportedly trying to distill Google's multi-trillion-parameter Gemini AI to run on iPhone

Apple is reportedly working to compress Google's multi-trillion-parameter Gemini AI model to run on iPhones, but the effort faces significant technical hurdles. The Gemini-infused Siri, expected later this year, will rely heavily on cloud processing from Google and Nvidia rather than running entirely on-device, marking a departure from Apple's stated privacy preference for local AI. The challenge stems from the vast size difference between cloud-based Gemini models and the smaller, quantized models that can fit in a smartphone's limited memory and processing environment.

read2 min publishedMay 28, 2026

It’s impossible to totally avoid generative AI when interacting with technology anymore, but Apple has a bit less of it. That’s not entirely by choice, though. The iPhone maker has delayed the AI-enhanced Siri multiple times since first promising it in 2024, but a deal with Google will merge the iconic assistant with Gemini later this year. As we approach WWDC, Apple has been working to bring big AI smarts to the modest processing environment of a smartphone. Apple fans may not like the outcome, though.

Apple has long crowed about the privacy value of running AI locally, but a new report suggests that despite Apple’s best efforts, the iPhone’s Gemini makeover will lean heavily on Google and Nvidia in the cloud. The Information reports that Apple’s Gemini-infused Siri will run both on-device and in the cloud, an apparent reversal of its privacy-focused preference for local AI.

With every new chip announcement, we hear about how the silicon has been optimized for AI—even Apple does this with its focus on Neural Engine upgrades. You may think from the grandiose language that smartphones are equipped to handle beefy AI models, but that’s not necessarily the case. In fact, the GPUs in most phones can process more AI tokens than the AI-focused NPUs. Components like Apple’s Neural Engine are designed for contextual, efficient AI processing. Even if phones had faster AI processing, they lack the RAM to keep enormous models in memory.

Even the largest AI models are still middling assistants, and that makes local AI very challenging. The AI models that run on phones are physically smaller, featuring at most a few billion parameters. Compare that to Google’s latest Gemini models, which have trillions of parameters, The Information reports. On-device AI models are also “quantized” to run at lower precision, making them faster but affecting the accuracy of token generation. This all adds up to AIs that feel less smart than their cloud brethren, and even big cloud-based models can be pretty dumb sometimes.

The amazing, shrinking Gemini #

Google does have versions of Gemini optimized for mobile devices, which it calls Gemini Nano. However, these are designed for powering contextual features like Magic Cue and audio summarization. Siri, on the other hand, is supposed to be a conversational assistant—you talk to it and it does things. That’s a different experience that requires a different kind of model. On Android, Google doesn’t even bother trying to do that locally. Talking to Gemini always goes straight to the cloud.

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