Exploring Local AI on the M5 Ultra Mac Studio: Promising Yet Challenging

Image: Illustrative image · Aleeexfernandez · Public domain · Source

A technology writer shares early experiences running large-scale local AI models on powerful hardware like Apple's M5 Ultra Mac Studio, highlighting privacy benefits and practical use cases, alongside challenges in usability and reliability.

As artificial intelligence tools become increasingly integrated into daily tasks, many users remain cautious about relying on cloud-based AI services due to privacy concerns. A recent firsthand account from The Verge details the experience of running large local AI models on high-end hardware — specifically Apple’s M5 Ultra Mac Studio with 256GB of unified memory. This setup enables the use of powerful models like the 125-billion parameter Qwen 3.8 Flash Next entirely offline.

The author experimented with Hermes Agent, an open-source AI agent desktop app available for macOS, Windows, and Linux, which supports running AI models locally without ongoing costs or data exposure to cloud providers. Although initial setup and model selection felt overwhelming due to the wide variety and sizes of available models, the author quickly managed to start using Hermes with Qwen for practical tasks.

Early implementations included making a daily morning briefing that collates email and calendar items along with weather updates. While this simplistic function required adjusting macOS power settings to avoid sleep during scheduled runs, it demonstrated potential for personalized automation. More compelling was leveraging the AI to organize a personal Steam game library of over 400 titles by genre and user-defined categories—tasking the AI via Steam’s web API with appropriate permissions, then revoking access after completion to preserve security.

The AI also assisted with financial data analysis and generating a laptop specification comparison spreadsheet, tasks for which the author preferred local processing to avoid uploading sensitive or embargoed information to the cloud. Efforts are underway to automate more complex routines, such as laptop benchmark testing, by guiding Hermes through scripting procedures, though this remains a work in progress.

Despite the enthusiasm, local AI models and agent systems are not yet seamless assistants. Breakdowns in task execution occur, such as failures in the daily briefing generation, underscoring the technology’s nascent stage. The author adopts a pragmatic stance, treating these AI tools as specialized software utilities rather than personal companions, focusing on controlled command-based interactions.

This exploration highlights both the promise and current limitations of local AI use on advanced computing hardware. It offers insights into practical applications that balance capability with privacy and control, while acknowledging the learning curve and occasional instability of today’s local AI environments. As hardware and software mature, such setups may become viable alternatives or complements to cloud-based AI solutions, especially for users prioritizing data security and offline functionality.