MLX on SoloSoft

MLX-Audio: TTS, STT, and STS Library Optimized for Apple Silicon

Mon, 01 Jan 0001 00:00:00 +0000

Apple Silicon Macs equipped with M-series chips – from the M1 through the latest M4 Ultra – pack extraordinary computational power, particularly for machine learning workloads. Their unified memory architecture allows models to access large amounts of fast memory without the bottlenecks of traditional CPU-GPU data transfer. MLX-Audio, an open-source Python library built on Apple’s MLX framework, is purpose-built to exploit this hardware advantage for all things audio AI.

MLX-Audio provides a unified interface for text-to-speech, speech-to-text, and speech-to-speech conversion, supporting dozens of models from OpenAI’s Whisper (for transcription) to Kokoro and VoiceCraft (for synthesis). It brings together capabilities that are typically scattered across multiple libraries and frameworks, all optimized to run efficiently on Mac hardware.

MLX-VLM: Vision Language Model Inference and Fine-Tuning on Apple Silicon

Mon, 01 Jan 0001 00:00:00 +0000

Running Vision Language Models – AI systems that can simultaneously understand images and text – has traditionally required expensive NVIDIA GPUs with substantial VRAM. Apple Silicon users were largely left out of the multimodal AI revolution, forced to rely on cloud APIs or dual-machine setups. MLX-VLM by developer Blaizzy changes this equation entirely.

MLX-VLM is an open-source Python package that brings Vision Language Model inference and fine-tuning directly to Apple Silicon hardware using Apple’s MLX framework. By leveraging the unified memory architecture of M-series chips, it enables Mac users to run sophisticated multimodal models – including LLaVA, Qwen-VL, InternVL2, and PaliGemma2 – entirely on-device, with performance that often surprises even experienced practitioners.

MLX: Apple's Machine Learning Framework for Apple Silicon

Mon, 01 Jan 0001 00:00:00 +0000

For years, machine learning on Macs meant one of two things: running PyTorch or TensorFlow through Apple’s Metal Performance Shaders backend, or accepting that NVIDIA-optimized frameworks would never fully leverage Apple Silicon’s capabilities. Both approaches left performance on the table. The unified memory architecture that makes M-series chips revolutionary for creative work went largely unused for ML.

MLX changes this entirely. It is Apple’s open-source ML framework, purpose-built for Apple Silicon. From the ground up, every optimization — lazy computation, unified memory access, neural engine integration — is designed for M-series hardware. The result is a framework that runs common ML workloads 2-3x faster on the same hardware compared to PyTorch through Metal, while using a cleaner, NumPy-inspired API.