# Apple MLX Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/apple-mlx
> Markdown URL: https://aitinkerers.org/technologies/apple-mlx.md
> Technology record last updated: 2026-03-01T10:25:00Z
> Generated: 2026-09-21T19:37:13Z

Apple MLX: An efficient, open-source array framework for machine learning, specifically engineered for the unified memory architecture of Apple silicon.

MLX is Apple's high-performance, open-source array framework, purpose-built for efficient machine learning on Apple silicon (M-series chips). It capitalizes on the unified memory architecture: data remains in shared memory, eliminating costly transfers between the CPU and GPU. The API is immediately familiar, closely mirroring NumPy for core operations and PyTorch for higher-level packages (e.g., `mlx.nn`). Developers use Python, C++, C, or Swift to leverage key features like composable function transformations (automatic differentiation) and dynamic graph construction. MLX enables researchers and developers to efficiently train and deploy large models locally, including LLaMA and Stable Diffusion.

- Official technology site: https://mlx-framework.org/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Big Models, Small Machines: Run Full-Precision LLMs on Low Memory](https://london.aitinkerers.org/talks/rsvp_xXYe9n5TkH0)

Running LLMs on edge devices often means choosing between expensive hardware or accuracy loss from quantization. This talk will present a custom inference strategy that enables running full-precision models on low-memory machines. For example, a 1.7B parameter model can run on an 8GB Mac at full precision with minimal memory footprint. Note that I have been able to run a SmolLM2-1.7B with as little as 250MB as an example. The demo will walk through the approach, show it in action, and discuss challenges/trade-offs and potential real-life applications, and next steps for improving.

- Event context: AI Tinkerers x MongoDB - Demopalooza — 2025-10-07 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_xXYe9n5TkH0

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