The push for on-device artificial intelligence continues to gain momentum, particularly within the Apple ecosystem. A new music generation application, LoopMaker, exemplifies this trend, running entirely on a Mac using Apple’s MLX framework. This approach bypasses the need for cloud-based processing and subscription models that have become commonplace in the AI music space.
Developed by Tarun Yadav, LoopMaker addresses a frustration with existing AI music tools like Suno, Stable Audio, and AIVA, which rely on server-side processing and recurring fees. Yadav’s solution, detailed on his website and shared on Reddit’s r/LocalLLaMA forum , offers a self-contained music creation experience. After an initial model download, the application requires no internet connection, ensuring user data remains private and workflows aren’t interrupted by subscription lapses.
The application is built natively in Swift for macOS and leverages the MLX framework for on-device inference. MLX, introduced by Apple in December 2023, is specifically designed for Apple Silicon chips, taking advantage of their unified memory architecture. This architecture allows the CPU and neural engine to share the same memory pool, reducing the performance bottlenecks associated with constantly transferring data between processors. As explained in a guide on fine-tuning LLMs on Mac, this shared memory access is a key advantage of MLX over traditional setups.
LoopMaker is optimized for M-series chips (M1, M2, M3, and M4) and supports the creation of tracks up to four minutes in length, with optional lyrics and vocals. It offers six genre modes: Lo-Fi, Cinematic, Ambient, Electronic, Hip-Hop, and Jazz. The developer reports that generation is “usable,” avoiding the lengthy processing times often associated with on-device AI.
This development arrives alongside broader advancements in utilizing MLX for various AI tasks. GitHub repository Flux Generator demonstrates the framework’s capabilities beyond music generation, extending to image generation with models like Black Forest Flux and Stable Diffusion, as well as Facebook’s MusicGen. Flux Generator highlights MLX’s performance benefits, noting significant speed improvements compared to other frameworks on Apple Silicon. It also emphasizes the framework’s suitability for fine-tuning models, a process that traditionally required substantial computational resources.
The ability to fine-tune large language models (LLMs) locally on Macs using MLX is becoming increasingly practical. A guide published in October 2025 details the process, noting that even an M1 Mac with 64GB of RAM can handle fine-tuning smaller models (7B to 13B parameters), particularly when using quantization techniques to reduce memory usage. Quantization reduces the precision of the model’s parameters, decreasing its size and memory footprint with a potentially small impact on accuracy. The guide estimates training a 7B model with 4-bit quantization can take 10-30 minutes for 1,000 iterations.
Apple’s commitment to on-device AI is further underscored by recent updates to Logic Pro, its professional digital audio workstation. , Apple announced new AI-backed features for Logic Pro, emphasizing the synergy between the software, iPad, Mac, and M-series Apple silicon. According to Apple, this combination provides “creative pros with the best music creation experience in the industry.”
The emergence of applications like LoopMaker and Flux Generator, coupled with Apple’s continued investment in MLX, signals a shift towards more accessible and private AI experiences. By enabling on-device processing, these tools empower users with greater control over their data and creative workflows. While the local AI music generation space is still in its early stages compared to the advancements in large language models, the foundation is being laid for a future where powerful AI capabilities are readily available directly on personal devices.
The developer of LoopMaker expressed curiosity about other approaches to on-device audio generation, suggesting a growing interest within the community to explore the possibilities of local AI music creation. The success of such applications will likely depend on continued optimization of the MLX framework and the development of efficient models that can deliver high-quality results on consumer hardware.
