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oMLX is working really well as single machine inference engine for coding agents! Caching is managed perfectly (it can use a ton of disk space, be aware!) and oQ quantization delivers great results. Behind the scenes it uses the standard MLX building blocks (75% created by Prince Canuma 🙏):...

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Ivan Fioravanti ᯅ

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19,258 просмотров • 1 месяц назад •via X (Twitter)

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RF-DETR by Roboflow now on MLX It can do realtime instance segmentation on-device and enable some cool use cases for visual analysis, monitoring and robotics like Reachy Mini. Also augmented VLM and VLA by preprocessing image and video with areas of interest. New release coming soon on mlx-vlm 🚀 For those who can’t wait you can install mlx-vlm from source.
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RF-DETR by Roboflow now on MLX It can do realtime instance segmentation on-device and enable some cool use cases for visual analysis, monitoring and robotics like Reachy Mini. Also augmented VLM and VLA by preprocessing image and video with areas of interest. New release coming soon on mlx-vlm 🚀 For those who can’t wait you can install mlx-vlm from source.

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30,499 просмотров • 2 месяцев назад

As part of our goal to make MLX a great research tool, we're expanding support to new languages like Swift and C, making experimentation on Apple silicon easier for ML researchers. Video generating text with Mistral 7B and MLX Swift 👇 MLX is an array framework for machine learning research on Apple silicon. MLX is intended for research and not for production deployment of models in apps.
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As part of our goal to make MLX a great research tool, we're expanding support to new languages like Swift and C, making experimentation on Apple silicon easier for ML researchers. Video generating text with Mistral 7B and MLX Swift 👇 MLX is an array framework for machine learning research on Apple silicon. MLX is intended for research and not for production deployment of models in apps.

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Kimi K2.6 was released 1h ago, and it looks amazing! Here it's running with MLX (mlx-vlm) on two M3 Ultras (full 1T param VLM) 🔥

Pedro Cuenca

65,485 просмотров • 1 месяц назад

M5 Max + ACE-Step v1.5 + MLX = 20 seconds to generate incredible music, commercial ready and locally generated 🤯 There is a native MLX backend in the repo, super easy to use! 🧵 Audio on!
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M5 Max + ACE-Step v1.5 + MLX = 20 seconds to generate incredible music, commercial ready and locally generated 🤯 There is a native MLX backend in the repo, super easy to use! 🧵 Audio on!

Ivan Fioravanti ᯅ

15,919 просмотров • 2 месяцев назад

mlx distributed uses mpi for machine to machine communication, and this is how you can handle uneven distribution of model layers. Thanks Awni Hannun for the help
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mlx distributed uses mpi for machine to machine communication, and this is how you can handle uneven distribution of model layers. Thanks Awni Hannun for the help

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cocktail peanut

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I wasn't expecting such an intelligence model on my local machine. Minimax 2.1 on MLX🔥 git clone cd mlx-lm && pip install -e . mlx_lm.server --model mlx-community/MiniMax-M2.1-4bit

mzba

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argmax

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OpenCode + MLX + Qwen3.5-397B-A17B-4bit. Video is 8x, but the goal is showing that It works! This is something unimaginable just few months ago. MLX Team is pushing like crazy and M5 Ultra will do the rest 🚀

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BOOM! Latest Qwen 30B A3B 2507 running blazingly fast on Mac powered by MLX 💥 mlx_lm.chat --model "lmstudio-community/Qwen3-30B-A3B-Instruct-2507-MLX-4bit" That's it, try it out today!

Vaibhav (VB) Srivastav

35,873 просмотров • 10 месяцев назад

A perfect coding model for MLX on Apple silicon.. Qwen delivered again. Runs quite fast on an M3 Ultra. Running the 4-bit quantized with mlx-lm:
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186,641 просмотров • 10 месяцев назад

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Day 2 of 3 MLX Releases: Introducing Local Computer-Use 🚀🔥 A powerful tool built with MLX that uses Vision Language models and Voice models to control your Mac through visual understanding, planning and reasoning. Features ⚡️Automate your workflow with natural language 😎 Control your computer “hands-free” This project now supports both: 🤖 Level 1 (GUI Agent) 🧠 Level 2 (Autonomous GUI Agent) Get started: > pip install -U mlx-vlm mlx-audio mlx-whisper Please leave us a star and send a PR :)

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Llama 3 running locally on iPhone with MLX Built by exo intern team Mohamed Baioumy h/t Awni Hannun MLX & Prince Canuma for the port

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295,803 просмотров • 2 лет назад

Qwen 2.5 Coder Q4 M4 Max Inference test. Apple MLX vs Ollama: - MLX: 23.97 toks/sec 🥇🔥 - Ollama: 18.33 toks/sec 🥈 Here a video to show results
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Qwen 2.5 Coder Q4 M4 Max Inference test. Apple MLX vs Ollama: - MLX: 23.97 toks/sec 🥇🔥 - Ollama: 18.33 toks/sec 🥈 Here a video to show results

Ivan Fioravanti ᯅ

34,881 просмотров • 1 год назад

Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)
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Day 1 of 3 MLX Releases: Introducing MLX-Audio 🚀🔥 A text-to-speech (TTS) and Speech-to-Speech (STS) library built on Apple's MLX framework, providing efficient speech synthesis on Apple Silicon. Features ⚡️Fast inference on Apple Silicon (M series chips) 🤖Multiple language support 🗣️Voice customization options 🚀Quantization support for optimized performance Supported models: 🪶Kokoro - A multilingual TTS model with 82M params that supports various languages and voice styles. With more models coming soon. Get started: > pip install mlx-audio Please leave us a star and send a PR :)

Prince Canuma

123,478 просмотров • 1 год назад

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Next mlx-vlm release will ship with continuous batching support on the server 🚀 What's coming: → Continuous batching — new requests join the active batch immediately, no waiting. Mixed image + text batches supported → OpenAI-compatible API — field-for-field match with mlx-lm, reasoning/content split for thinking models, tag-aware streaming → Multi-turn tool calling — full tool use support across streaming and non-streaming, works with Gemma4 and other templates → Vision feature caching — cache image embeddings across turns. Gemma4: 228x speedup, Qwen3.5: 23x on cache hit All running locally on Apple Silicon. Check our this demo running 4 concurrent requests (mixed image + text) to gemma-4-26B-A4B-IT by Google Gemma in bf16 using Pi + MLX-VLM server on my M3 Ultra. One of the requests ingests a 8K resolution image!

Prince Canuma

82,085 просмотров • 1 месяц назад

Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:
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Sam-Audio by AI at Meta is now on MLX-Audio 🔊 You can now isolate voices or sounds from any audio locally in your Mac. I added this simple UI to the MLX-Audio studio app to help you get started Note: - The release is coming soon for now please install from source. - Vision part was excluded for now. Let me know if you need it. - PR for UI:

Prince Canuma

19,027 просмотров • 5 месяцев назад

The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:
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The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:

Awni Hannun

42,664 просмотров • 10 месяцев назад

Pretty cool that with the new Qwen 2.5 models you can ask questions / generate using a reasonably sized code-base as context, all running on a laptop with mlx-lm. The 7B runs pretty fast on an M4 Max using the mlx-lm code base (~16k lines) as context:
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Pretty cool that with the new Qwen 2.5 models you can ask questions / generate using a reasonably sized code-base as context, all running on a laptop with mlx-lm. The 7B runs pretty fast on an M4 Max using the mlx-lm code base (~16k lines) as context:

Awni Hannun

27,442 просмотров • 1 год назад