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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...

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Adrien Grondin

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13,376 views • 20 days ago •via X (Twitter)

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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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DeepSeek R1 distilled to Qwen 1.5B easily runs on my iPhone 16 with MLX swift. Here's the 4-bit model reasoning entirely on device at almost 60 toks/sec:
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DeepSeek R1 distilled to Qwen 1.5B easily runs on my iPhone 16 with MLX swift. Here's the 4-bit model reasoning entirely on device at almost 60 toks/sec:

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Llama 3.2 1B in 4-bit runs at ~60 toks/sec with MLX Swift on my iPhone 15 pro. It's quite good and easily runs on-device:
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Another demo of the iPhone 17 Pro’s on-device LLM performance This time with Ling mini 2.0 by InclusionAI, a 16B MoE model with 1.4B active parameters running at ~120tk/s Thanks to Awni Hannun for the MLX DWQ 2-bit quants
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Another demo of the iPhone 17 Pro’s on-device LLM performance This time with Ling mini 2.0 by InclusionAI, a 16B MoE model with 1.4B active parameters running at ~120tk/s Thanks to Awni Hannun for the MLX DWQ 2-bit quants

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I think seawater did something… strange to my iPhone 17 Pro Max charging port. Now when I plug in the charger and turn on the switch at the same time, it shows charging a bit late compared to the iPhone 16 Pro Max and 15 Pro Max. Today I also noticed it does not charge at all with a 25W charger, but it works with 45W and 65W chargers. Should I go to the Apple Store and replace the charging port, or just use the chargers that work? Will this problem get worse over time?
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I think seawater did something… strange to my iPhone 17 Pro Max charging port. Now when I plug in the charger and turn on the switch at the same time, it shows charging a bit late compared to the iPhone 16 Pro Max and 15 Pro Max. Today I also noticed it does not charge at all with a 25W charger, but it works with 45W and 65W chargers. Should I go to the Apple Store and replace the charging port, or just use the chargers that work? Will this problem get worse over time?

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Someone got DeepSeek-R1-0528-Qwen3-8B running on an iPhone 16 Pro using MLX. It runs but takes ages to respond, and the phone gets hot fast. 8B models on phones aren't sci-fi anymore. via u/adrgrondin on r/LocalLLaMA
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GLM-4.7 Flash by Z.ai running on M3 Ultra using MLX! 4bit at 81 toks/s 🔥 8bit at 64 toks/s 🔥 (video below) Model converted from transformers to MLX on the fly with Codex + GPT-5.2 HIgh!
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Google’s Gemma 4 E2B running on-device on iPhone 17 Pro Gemma 4 is built from the same research as Gemini 3, has image understanding capabilities and can reason if needed Running at ~40tk/s with MLX optimized for Apple Silicon
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Introducing MLX-Swift-TS An SDK for running time series foundation models fully on-device on Apple Silicon. When I joined Datadog, Inc. , I was introduced to Toto, our time series foundation model, and got excited about zero-shot forecasting across different domains. While building a health copilot app, I realized there wasn’t a simple way to run models like these locally on device. So I built one. MLX-Swift-TS exposes a common TimeSeriesForecaster interface for loading and running multiple time series architectures directly in Swift using MLX. No server required. The attached video shows on-device forecasting running inside a native Swift app. Huge thanks to Awni Hannun and the MLX team for building MLX and its Swift API, Prince Canuma for inspiration on MLX SDK patterns, and Ameet Talwalkar and the Datadog team for Toto.

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