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Rapid-MLX 0.13.0 is out 🚀 Rapid-MLX 0.13.0 is officially here! 🚀 Run massive models locally on your Mac with sub-second replies and side-by-side dictation + chat. 100% on-device. 100% MLX. Get it now via DMG or CLI: - - curl -fsSL | bash

Rapid-MLX · local AI on your Mac

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31,461 次观看 • 14 天前 •via X (Twitter)

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🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

raullen

22,765 次观看 • 3 个月前

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

raullen

24,988 次观看 • 5 个月前

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.

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.

Prince Canuma

30,499 次观看 • 5 个月前

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:

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 次观看 • 8 个月前

Excited to introduce Nativ 🚀 Run frontier open models locally on your Mac. No accounts, no subscriptions, no cloud. ⚡ Built on mlx-vlm — multimodal + fastest on Apple Silicon 🔒 100% private — every token generated on your machine 🛠 Plug your coding agents into a local endpoint 📊 Live telemetry — tokens/sec, memory, usage 🆓 100% open source, MIT licensed, free forever Your Mac is more capable than you think. Stop renting intelligence. Download for macOS 👇 Github repo 🌟

Excited to introduce Nativ 🚀 Run frontier open models locally on your Mac. No accounts, no subscriptions, no cloud. ⚡ Built on mlx-vlm — multimodal + fastest on Apple Silicon 🔒 100% private — every token generated on your machine 🛠 Plug your coding agents into a local endpoint 📊 Live telemetry — tokens/sec, memory, usage 🆓 100% open source, MIT licensed, free forever Your Mac is more capable than you think. Stop renting intelligence. Download for macOS 👇 Github repo 🌟

Prince Canuma

129,200 次观看 • 1 个月前

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:

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

42,810 次观看 • 1 年前

MiniMax M3 support added to mlx-vlm with MSA implementation! 🚀 Tested on M3 Ultra 512GB running at 24 tps with peak memory ~240GB. Now working on optimizing performance and adding ton of tests 💪 Model is here: PR is here:

MiniMax M3 support added to mlx-vlm with MSA implementation! 🚀 Tested on M3 Ultra 512GB running at 24 tps with peak memory ~240GB. Now working on optimizing performance and adding ton of tests 💪 Model is here: PR is here:

Ivan Fioravanti ᯅ

24,376 次观看 • 2 个月前

The new Qwen 3.5 by Qwen running on-device on iPhone 17 Pro. Qwen 3.5 beats models 4 times its size, has strong visual understanding, and can toggle reasoning on or off. The 2B 6-bit model here is running with MLX optimized for Apple Silicon.

The new Qwen 3.5 by Qwen running on-device on iPhone 17 Pro. Qwen 3.5 beats models 4 times its size, has strong visual understanding, and can toggle reasoning on or off. The 2B 6-bit model here is running with MLX optimized for Apple Silicon.

Adrien Grondin

3,558,786 次观看 • 6 个月前

Alibaba’s Qwen 3.5 is now running fully on-device on the iPhone 17 Pro. It outperforms models 4x its size, delivers strong visual understanding, and lets you switch reasoning on or off. This demo uses the 2B 6-bit version, optimized with MLX for Apple Silicon.

Alibaba’s Qwen 3.5 is now running fully on-device on the iPhone 17 Pro. It outperforms models 4x its size, delivers strong visual understanding, and lets you switch reasoning on or off. This demo uses the 2B 6-bit version, optimized with MLX for Apple Silicon.

Hugging Models

203,055 次观看 • 5 个月前

Jarvis is can speak! 🚀 I’m running Chatterbox-Turbo from Resemble AI on my Mac using MLX-Audio as a server Now I’m gonna refine it and share it later 🔥 PS: don’t mind my voice it just came back 2 days ago 😅 Repo:

Jarvis is can speak! 🚀 I’m running Chatterbox-Turbo from Resemble AI on my Mac using MLX-Audio as a server Now I’m gonna refine it and share it later 🔥 PS: don’t mind my voice it just came back 2 days ago 😅 Repo:

Prince Canuma

47,714 次观看 • 8 个月前

Gemma 4 now works on-device using React Native! You can now run Gemma 4 fully offline in your cross-platform apps with local hardware acceleration: ⚡ Vulkan delegate on Android ⚡ MLX delegate on Apple Silicon See Gemma 4's vision and tool-use capabilities in action, instantly reading a flyer and scheduling a calendar event, 100% on-device.

Gemma 4 now works on-device using React Native! You can now run Gemma 4 fully offline in your cross-platform apps with local hardware acceleration: ⚡ Vulkan delegate on Android ⚡ MLX delegate on Apple Silicon See Gemma 4's vision and tool-use capabilities in action, instantly reading a flyer and scheduling a calendar event, 100% on-device.

Google Gemma

116,179 次观看 • 2 个月前

Your Mac is about to run inference like a datacenter. Coming soon to MLX-Swift: Continuous batching: the fastest way to handle multiple inference streams locally. It starts with regular inference and seamlessly upgrades to batched mode when new requests arrive. The best of both worlds. Based on the work of and Awni Hannun

Your Mac is about to run inference like a datacenter. Coming soon to MLX-Swift: Continuous batching: the fastest way to handle multiple inference streams locally. It starts with regular inference and seamlessly upgrades to batched mode when new requests arrive. The best of both worlds. Based on the work of and Awni Hannun

Ronald Mannak

60,570 次观看 • 10 个月前

Maxed out M3 Ultra is here! 🚀 Huge thank you to the entire MLX community for making this possible. With your support, I can dedicate myself full-time to advancing MLX as an independent developer. For the first time, I have the hardware to match the ambition—no more bottlenecks holding back the biggest models and most challenging work. Excited for what’s ahead. Let’s build something incredible together.

Maxed out M3 Ultra is here! 🚀 Huge thank you to the entire MLX community for making this possible. With your support, I can dedicate myself full-time to advancing MLX as an independent developer. For the first time, I have the hardware to match the ambition—no more bottlenecks holding back the biggest models and most challenging work. Excited for what’s ahead. Let’s build something incredible together.

Prince Canuma

27,880 次观看 • 10 个月前

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️

raullen

34,148 次观看 • 29 天前

GLM-5.2 8bit running on two M3 Ultra 512GB with MLX distributed? Here it is! 🚀 Decode speed: 17.9 tokens/sec 🔥 Memory used: ~ 760GB 👀 Again keep in mind it's a preliminary PR by super Pedro Cuenca still a WIP!

GLM-5.2 8bit running on two M3 Ultra 512GB with MLX distributed? Here it is! 🚀 Decode speed: 17.9 tokens/sec 🔥 Memory used: ~ 760GB 👀 Again keep in mind it's a preliminary PR by super Pedro Cuenca still a WIP!

Ivan Fioravanti ᯅ

34,175 次观看 • 2 个月前

Qwen3.6 27B landed yesterday, so I ran it on 4 setups side-by-side to see how they stack up: 🔴 RTX 4090 — 45.59 tok/s, TTFT 525ms 🟢 RTX 5090 — 51.83 tok/s, TTFT 752ms ⚫️ M2 Ultra — 22.30 tok/s, TTFT 216ms 🟣 DGX Spark — 11.08 tok/s, TTFT 319ms This is a standard test: no tuning, just the out-of-the-box experience. For the NVIDIA cards I used llama.cpp with Unsloth's UD-Q4_K_XL quant. For the M2 Ultra I used MLX with Unsloth's UD-MLX-4bit quant, since MLX is the native path on Apple Silicon. Please consider this as the baseline, you can definitely squeeze more out of every one of these with fine-tuned settings.

Qwen3.6 27B landed yesterday, so I ran it on 4 setups side-by-side to see how they stack up: 🔴 RTX 4090 — 45.59 tok/s, TTFT 525ms 🟢 RTX 5090 — 51.83 tok/s, TTFT 752ms ⚫️ M2 Ultra — 22.30 tok/s, TTFT 216ms 🟣 DGX Spark — 11.08 tok/s, TTFT 319ms This is a standard test: no tuning, just the out-of-the-box experience. For the NVIDIA cards I used llama.cpp with Unsloth's UD-Q4_K_XL quant. For the M2 Ultra I used MLX with Unsloth's UD-MLX-4bit quant, since MLX is the native path on Apple Silicon. Please consider this as the baseline, you can definitely squeeze more out of every one of these with fine-tuned settings.

stevibe

104,692 次观看 • 4 个月前

GLM-4.7-8bit (350GB) running at 19 toks/s on two M3 Ultra 512GB using Tensor Parallelism with EXO - MLX, versus 14 toks/s with single node. 🚀 Now context benchmarking & then OpenCode tests 🔥 Note: this is from sources, I had to change things to run it.

GLM-4.7-8bit (350GB) running at 19 toks/s on two M3 Ultra 512GB using Tensor Parallelism with EXO - MLX, versus 14 toks/s with single node. 🚀 Now context benchmarking & then OpenCode tests 🔥 Note: this is from sources, I had to change things to run it.

Ivan Fioravanti ᯅ

327,687 次观看 • 8 个月前

Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️

Day 1 of 3 days of MLX: Introducing MLX-Audio-Swift SDK 🚀 A modular Swift SDK for voice agents and tasks on Apple Silicon built by Lucas Newman and yours truly. iOS, macOS, and visionOS developers can now build native apps with real-time, on-device audio intelligence: 🗣️ Text-to-Speech (TTS) 👂 Speech-to-Text (STT) 🔄 Speech-to-Speech (STS) 🎙️ Voice Activity Detection (VAD) and more. Only import the capabilities you need, nothing extra. Get started today and leave us a star ⭐️

Prince Canuma

163,831 次观看 • 6 个月前

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!

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

82,349 次观看 • 4 个月前

Introducing Private Mind! 🚀 Our new open-source app, built with React Native ExecuTorch, lets you run LLMs 100% locally on your device. 🔹 Explore and swap pre-exported models 🔹 Benchmark performance, speed & memory 🔹 Customize system prompts 🔹 100% offline, private & free Now available on App Store and Google Play! Links in 🧵

Introducing Private Mind! 🚀 Our new open-source app, built with React Native ExecuTorch, lets you run LLMs 100% locally on your device. 🔹 Explore and swap pre-exported models 🔹 Benchmark performance, speed & memory 🔹 Customize system prompts 🔹 100% offline, private & free Now available on App Store and Google Play! Links in 🧵

Software Mansion

21,764 次观看 • 1 年前