mlx
17 articles about mlx in AI news
MLX-LM v0.9.0 Adds Better Batching, Supports Gemma 4 on Apple Silicon
Apple's MLX-LM framework released version 0.9.0 with enhanced server batching and support for Google's Gemma 4 model, improving local LLM inference efficiency on Apple Silicon. This update addresses a key performance bottleneck for developers running models locally on Mac hardware.
Gemma 4 Ported to MLX-Swift, Runs Locally on Apple Silicon
Google's Gemma 4 language model has been ported to the MLX-Swift framework by a community developer, making it available for local inference on Apple Silicon Macs and iOS devices through the LocallyAI app.
mlx-vlm v0.4.4 Launches with Falcon-Perception 300M, TurboQuant Metal Kernels & 1.9x Decode Speedup
The mlx-vlm library v0.4.4 adds support for TII's Falcon-Perception 300M vision model and introduces TurboQuant Metal kernels, achieving up to 1.9x faster decoding with 89% KV cache savings on Apple Silicon.
Gemma 4 26B A4B Hits 45.7 tokens/sec Decode Speed on MacBook Air via MLX Community
A community benchmark shows the Gemma 4 26B A4B model running at 45.7 tokens/sec decode speed on a MacBook Air using the MLX framework. This highlights rapid progress in efficient local deployment of mid-size language models on consumer Apple Silicon.
Roboflow's RF-DETR Model Ported to Apple MLX, Enabling Real-Time On-Device Instance Segmentation
Roboflow's RF-DETR object detection model is now available on Apple's MLX framework, enabling real-time instance segmentation on Apple Silicon devices. This port unlocks new on-device visual analysis applications for robotics and augmented vision-language models.
Ollama Now Supports Apple MLX Backend for Local LLM Inference on macOS
Ollama, the popular framework for running large language models locally, has added support for Apple's MLX framework as a backend. This enables more efficient execution of models like Llama 3.2 and Mistral on Apple Silicon Macs.
Sam3 + MLX Enables Local, Multi-Object Video Tracking Without Cloud APIs
A developer has combined Meta's Segment Anything 3 (Sam3) with Apple's MLX framework to enable local, on-device object tracking in videos. This bypasses cloud API costs and latency for computer vision tasks.
Facebook's SAM 3 Vision Model Ported to Apple's MLX Framework, Enabling Real-Time Tracking on M3 Max
Facebook's Segment Anything Model 3 (SAM 3) has been ported to Apple's MLX framework, enabling real-time object tracking on an M3 Max MacBook Pro. This demonstrates efficient on-device execution of a foundational vision model without cloud dependency.
mlx-vlm v0.4.2 Adds SAM3, DOTS-MOCR Models and Critical Fixes for Vision-Language Inference on Apple Silicon
mlx-vlm v0.4.2 released with support for Meta's SAM3 segmentation model and DOTS-MOCR document OCR, plus fixes for Qwen3.5, LFM2-VL, and Magistral models. Enables efficient vision-language inference on Apple Silicon via MLX framework.
Qwen3-TTS Added to mlx-tune, Enabling Full Qwen Model Fine-Tuning on Apple Silicon Macs
The mlx-tune library now supports Qwen3-TTS, making the entire Qwen model stack—including the new text-to-speech model—fine-tunable on Apple Silicon Macs. This expands local AI development options for researchers and developers.
TurboQuant Ported to Apple MLX, Claims 75% Memory Reduction with Minimal Performance Loss
Developer Prince Canuma has successfully ported the TurboQuant quantization method to Apple's MLX framework, reporting a 75% reduction in memory usage with nearly no performance degradation for on-device AI models.
RunAnywhere's MetalRT Engine Delivers Breakthrough AI Performance on Apple Silicon
RunAnywhere has launched MetalRT, a proprietary GPU inference engine that dramatically accelerates on-device AI workloads on Apple Silicon. Their open-source RCLI tool demonstrates sub-200ms voice AI pipelines, outperforming existing solutions like llama.cpp and Apple's MLX.
X Post Reveals Audible Quality Differences in GPU vs. NPU AI Inference
A developer demonstrated audible quality differences in AI text-to-speech output when run on GPU, CPU, and NPU hardware, highlighting a key efficiency vs. fidelity trade-off for on-device AI.
Open-Source AI Assistant Runs Locally on MacBook Air M4 with 16GB RAM, No API Keys Required
A developer showcased a complete AI assistant running entirely on a MacBook Air M4 with 16GB RAM, using open-source models with no cloud API calls. This demonstrates the feasibility of capable local AI on consumer-grade Apple Silicon hardware.
Apple Silicon Achieves Near-Lossless LLM Compression at 3.5 Bits-Per-Weight, Claims Independent Tester
Independent AI researcher Matthew Weinbach reports achieving near-lossless compression of large language models on Apple Silicon, storing models at 3.5 bits-per-weight while maintaining within 1-2% quality of bf16 precision.
Insanely Fast Whisper CLI Transcribes 2.5 Hours of Audio in 98 Seconds with Flash Attention 2
A new open-source CLI tool called Insanely Fast Whisper achieves 19x speedup over standard Whisper large-v3, transcribing 150 minutes of audio in 98 seconds using Flash Attention 2 and batching with no quality loss.
Kimi 2.5's 1T Parameter MoE Model Runs on 96GB Mac Hardware via SSD Streaming
Developers have demonstrated that Kimi 2.5's 1 trillion parameter Mixture-of-Experts model can run on Mac hardware with just 96GB RAM by streaming expert weights from SSD, with only 32B parameters active per token.