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With iOS 27 Apple introduced its most powerful on-device model: AFM 3 Core Advanced It’s a 20 billion parameters model activating only between 1 to 4 billion parameters depending on the request Here’s the model running on iPhone 17 Pro

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Adrien Grondin

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472,311 次观看 • 1 个月前 •via X (Twitter)

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Here’s Apple’s foundation model running on iPhone 17 Pro It’s just so fast Apple was not joking, the A19 Pro chip is really good for running LLMs on-device
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Here’s Apple’s foundation model running on iPhone 17 Pro It’s just so fast Apple was not joking, the A19 Pro chip is really good for running LLMs on-device

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How does Edge0 run a 35B model with only 1–2.5 GB of peak memory? In simple terms: • The full model stays in storage. • Only the parameters needed for the current step are loaded into memory. • The next parameters are prepared while the current step is running. • Memory is reused instead of loading the entire model at once. Large model. Small working set. Fully on-device.
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How does Edge0 run a 35B model with only 1–2.5 GB of peak memory? In simple terms: • The full model stays in storage. • Only the parameters needed for the current step are loaded into memory. • The next parameters are prepared while the current step is running. • Memory is reused instead of loading the entire model at once. Large model. Small working set. Fully on-device.

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Another demo of Apple 20B on-device foundation model that comes with iOS 27 The new models support image understanding (on all iPhone with the new OS) Very good at OCR
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Another demo of Apple 20B on-device foundation model that comes with iOS 27 The new models support image understanding (on all iPhone with the new OS) Very good at OCR

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> be Muse Spark Open > Meta's Llama 4 disappoints everyone > Meta goes closed with Muse Spark > Zuck writes a $100-200 billion check > Muse Wonder ships on Monday > 3 days later Meta drops you with open weights > 816 billion parameters > only 42 billion active at a time > 1 million token context > built for coding and long agent tasks > runs on two Mac Studios on a desk > Meta calls you the most capable US open-weight model ever > every Chinese lab reading the model card America is back in the open-weight game.
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Apple wasn’t kidding, the iPhone 17 Pro is really built for running LLMs Here’s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible
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Apple wasn’t kidding, the iPhone 17 Pro is really built for running LLMs Here’s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible

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I wanted to try Maple-Preview by DeepGrove on iPhone so I simply ported the model to MLX Swift using Bionic Maple runs at ~80tk/s on iPhone 17 Pro with my quick port, it’s a 20B-A1B model at 2-bit The model is close to Qwen3.5 35-A2B in many benchs and runs easily on iPhone
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I wanted to try Maple-Preview by DeepGrove on iPhone so I simply ported the model to MLX Swift using Bionic Maple runs at ~80tk/s on iPhone 17 Pro with my quick port, it’s a 20B-A1B model at 2-bit The model is close to Qwen3.5 35-A2B in many benchs and runs easily on iPhone

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Guys, I translated the model into English, but unfortunately, for some reason, the parameters were hidden when I added it - I'm so sorry! Here’s how to translate the model into English and reveal the customization parameters.

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Side by side comparison of Apple’s on-device foundation model iPhone 16 Pro vs 17 Pro ~55 tk/s for the 16 Pro and ~85 tk/s for the 17 Pro

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