Apple released updated Mac mini and Mac Studio with new M6 and M5 Ultra chips, positioning them as machines for running large language models locally. The hardware refresh is purely specs-focused, but Apple's marketing now emphasizes AI workloads after macOS improvements last year made multi-Mac setups viable for distributed inference.

• Apple shipped macOS 26.2 in December with Thunderbolt 5 support for low-latency distributed AI inference, which triggered developer interest in daisy-chaining multiple Macs for larger models
• The M6 is Apple's first 2nm chip for Macs, and the M5 Ultra is now the most powerful option in the lineup, especially for AI tasks
• Developers and researchers are using stacked Mac minis and Studios as an alternative to expensive Nvidia GPU hardware for running local LLMs that exceed single-device capacity

This article compares running AI agents locally on a Mac Mini with Ollama and open-source models versus hosting them on a cloud server using Claude or Gemini APIs. It breaks down upfront and monthly costs—about $35/month local amortized versus roughly $73 for Gemini and $123 for Claude—and highlights performance, privacy, and usage trade-offs.

• Local Mac Mini setup costs ~$35/month amortized over 3 years vs ~$73/month for Gemini API or ~$123/month for Claude API on a cloud VPS
• Over a year, local stays around $420 while Gemini runs ~$876 and Claude ~$1,476
• Local open-source models handle summarization, document search, and private data well but lag behind Claude/Gemini on coding, complex reasoning, and long-context tasks
• Cloud pay-per-use makes sense for occasional agent use, while local hardware pays off for daily or nonstop operation