MLX on Landry Zetam - Tech Blog

NVIDIA vs Apple Silicon: The Architecture Battle That Shapes AI Models

Mon, 11 Aug 2025 00:00:00 +0000

When I tell people I run AI models on Mac Studios instead of NVIDIA GPUs, I often get puzzled looks. “But NVIDIA is the gold standard!” they say. And they’re not wrong — but they’re also not seeing the full picture. The architectural differences between NVIDIA and Apple Silicon aren’t just technical minutiae; they fundamentally change how AI models work, how they’re trained, and what hardware they run best on.

Building a Local AI Cluster with Mac Hardware: From Vision to Reality

Fri, 08 Aug 2025 00:00:00 +0000

The Mission That Started It All

Note: This post combines the journeys documented in my Mac AI Cluster Project and GPUStack Mac Clustering Tutorial. For deep technical details, see those project pages.

When I first discovered EXO Labs, their mission resonated deeply with me.

Humanity is headed toward a world controlled by a handful of companies that will dictate the AI models everyone uses. The real threat isn’t AI destroying us — it’s losing sovereignty over our own minds.

Powering Open WebUI and Cline with GPUStack: My AI Infrastructure Journey

Fri, 08 Aug 2025 00:00:00 +0000

After building my Mac AI cluster, I needed a way to make it accessible to all my tools. The journey took me through several frameworks before landing on the perfect solution: GPUStack providing an OpenAI-compatible API that seamlessly integrates with everything.

The Evolution: Finding the Right Framework

The Journey

My path to the current setup wasn’t straightforward:

MLX Distributed Computing: Started here for model training and local inference on Apple Silicon
Exo Labs: Experimented with this promising framework, but pivoted when the community began a redesign
GPUStack: The solution that finally clicked - providing OpenAI-compatible endpoints that work with everything

The Current Architecture

Here’s how my AI infrastructure actually works: