Tagged "mlx-framework"
14 articles tagged mlx-framework, 25 March 2026 to 7 October 2026. Newest first.
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Ollama v0.40.0 Brings Multimodal Embeddings and MLX Default on Apple Silicon
Ollama's latest release makes MLX the default runtime for supported models on Apple Silicon and implements native multimodal embedding support through EmbeddingGemma2Model architecture. This brings efficient text, audio, and video embeddings to on-device deployment with significantly reduced memory footprint.
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Apple Updates Creator Studio with AI Video Editing, Image Generation, and Logic Pro Enhancements
Apple expands its Creator Studio with new on-device AI capabilities for video editing and image generation, demonstrating the trend toward consumer-friendly local AI inference on Apple Silicon hardware.
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Apple iPad Air with M4 Chip Drops to $1349; Powerful On-Device LLM Inference Now More Accessible
Apple's M4-equipped iPad Air becomes more price-accessible at $1349, offering tablet users powerful local LLM inference capabilities through MLX and other frameworks. The M4 chip's performance metrics make it suitable for running 7B and 13B parameter models.
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Apple's M5 MacBook Air Advances On-Device AI with Redesigned Hardware
Apple's newly redesigned MacBook Air with the M5 chip emphasizes on-device AI capabilities, providing powerful local inference hardware for developers and users running large language models.
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Running AI Models Locally on M4 Processors with 24GB Memory
A technical guide explores deploying language models on Apple M4 devices with 24GB unified memory, demonstrating Apple Silicon's capabilities for local inference. The approach leverages frameworks optimized for ARM architecture and unified memory access.
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Mlx-serve: Run LLMs Natively on Your Mac
A new tool enabling native LLM inference on Apple Silicon Macs, leveraging MLX for optimized on-device deployment without external API dependencies.
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I Replaced My Local LLM With a Model Half Its Size and Got Better Results
Case study demonstrating that model size isn't the only factor determining performance—proper quantization, fine-tuning, and hardware matching can yield superior results with significantly smaller models.
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Llama 4 Scout on MLX: The Complete Apple Silicon Guide (2026)
An updated guide for running Llama 4 Scout models on Apple Silicon using MLX, covering optimization techniques and practical deployment patterns for macOS-based local LLM inference.
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DFlash Speculative Decoding Achieves 3.3x Speedup on Apple Silicon
A native MLX implementation of DFlash speculative decoding reaches 85 tokens/second on Qwen 3.5-9B running on Apple M5 Max, delivering a 3.3x performance boost through parallel draft token generation and single-pass verification.
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Kokoro TTS Achieves 20× Realtime Speed on CPU-Only On-Device Inference
A developer has successfully deployed Kokoro text-to-speech with 20× realtime performance using only CPU inference via MLX Swift on iOS, enabling high-quality, low-latency speech synthesis entirely on-device.
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Apple Silicon Macs Run Local AI Faster with Ollama's New MLX Support
Ollama now supports MLX, Apple's machine learning framework, enabling significantly faster local LLM inference on Apple Silicon Macs. This integration optimizes performance for M-series chips and makes local AI deployment more accessible to Mac users.
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Ollama Adopts Apple's MLX Framework for Faster Local AI on Mac
Ollama now leverages Apple's MLX framework to significantly improve inference speed on Apple silicon Macs through unified memory optimization. This integration makes running large language models locally more efficient and accessible for Mac users.
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mlx-Code: Run Claude Code Locally with MLX-LM
A new tool enables running Claude's code generation capabilities locally on Apple Silicon using MLX-LM, bringing powerful AI-assisted coding to on-device inference without cloud dependencies.
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Google TurboQuant: Extreme Compression for Local LLM Deployment
Google Research releases TurboQuant, a new quantisation technique enabling extreme model compression for efficient local and edge inference. Early implementations are already being integrated into frameworks like MLX Studio.