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Adrien Grondin

@adrgrondin7,798 subscribers

Apple Platforms @lmstudio building @LocallyAIApp, Prev. iOS Developer at @Match @MeeticGroup

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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

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

1,042,852 Aufrufe

Google's Gemma 4 26B A4B running on MacBook Pro M5 Max 100% native Swift Achieving over 100tk/s with Apple MLX framework

Google's Gemma 4 26B A4B running on MacBook Pro M5 Max 100% native Swift Achieving over 100tk/s with Apple MLX framework

349,410 Aufrufe

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

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

1,565,317 Aufrufe

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

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

881,917 Aufrufe

Demo of 1-bit Bonsai 8B from PrismML running on-device on iPhone 17 Pro More than 40tk/s for a dense 8B model on iPhone, that’s a first Powered by Apple MLX and available now in Locally AI

Demo of 1-bit Bonsai 8B from PrismML running on-device on iPhone 17 Pro More than 40tk/s for a dense 8B model on iPhone, that’s a first Powered by Apple MLX and available now in Locally AI

159,705 Aufrufe

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

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

493,370 Aufrufe

Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast

Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast

32,556 Aufrufe

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

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

46,205 Aufrufe

Really impressed by quality of Marvis-AI made by Prince Canuma & Lucas Newman, it's only 250M and uses less than 500MB in 4-bit 🤯 You only need 2 lines of code to run on macOS: pip install -U mlx-audio python -m mlx_audio.tts.generate --model Marvis-AI/marvis-tts-250m-v0.1 --stream \ --text "Hello there!" No reason not to give it a try!

Really impressed by quality of Marvis-AI made by Prince Canuma & Lucas Newman, it's only 250M and uses less than 500MB in 4-bit 🤯 You only need 2 lines of code to run on macOS: pip install -U mlx-audio python -m mlx_audio.tts.generate --model Marvis-AI/marvis-tts-250m-v0.1 --stream \ --text "Hello there!" No reason not to give it a try!

11,780 Aufrufe

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