Mac Studio power barely shifts local LLM math

This is less a product story than a reality check on local AI infrastructure economics. Electricity is the easy part; the harder question is whether owned hardware gives you enough throughput and model quality to beat a hosted subscription in real work.

• –Apple is now explicitly pitching Mac Studio for local AI workloads, saying the M3 Ultra model can run LLMs with hundreds of billions of parameters in memory and deliver much faster LM Studio token generation than older Mac Studio generations
• –The strongest replies argue Claude Opus-class models still outperform what most users can run locally, so comparing only monthly bills misses the real productivity tradeoff
• –Multiple commenters say power costs are close to negligible, with one estimating roughly $10 a month even under continuous 24/7 use
• –The real decision is capex versus opex: Mac Studio buys privacy, control, and fixed-cost compute, but only pays off if you keep it busy and can tolerate quantization, lower-end models, or multi-box setups