A Reddit user spent $2,498 on an M3 Max MacBook Pro (36GB) and benchmarked four local AI inference engines — software that loads a model and generates text — overnight: oMLX, Rapid-MLX, Splash, and MTPLX. Results: the MoE version (Mixture of Experts, an architecture that activates only a subset of parameters per pass) hit 110.2 tok/s (tokens per second), while the dense 27B model reached 31.7 tok/s. This is the first time a consumer laptop has crossed into daily-usable speed.
What this is
Local AI inference has long needed a chip with enough memory bandwidth (~300 GB/s) to unlock usable performance; the M3 Max is the first to deliver conversational-grade response from 35B-class models. What we have to flag: the four tools split the field — Rapid-MLX is fastest on MoE (110.2 tok/s), Splash is fastest on dense (31.7 tok/s) — meaning the local AI tooling ecosystem is still fragmenting, not consolidating.
Industry view
Supporters frame this as the tipping point for SMB on-prem deployment. Data-sensitive verticals like healthcare, legal, and finance used to have no choice but to hand data to cloud vendors; a $2,500 laptop can now run enterprise-grade models with data never leaving the building.
The objections deserve equal airtime: one, $2,500 isn't cheap for most employees; two, the tooling ecosystem hasn't consolidated, so picking the wrong stack means relearning from scratch; three, the original Reddit quote — "fine on my desk, don't even think about a library" — because fan noise is noticeable; four, cloud APIs keep dropping in price sharply, gradually compressing the cost edge of on-prem setups. The real moat for local AI is privacy and control, not price.
Impact on regular people
For enterprise IT: data-sensitive industries can let staff run models locally on Macs, with compliance reviews no longer needing to be redone for every deployment.
For individual professionals: any Mac with M2 Pro or above and 32GB of RAM can run 13B models offline via Ollama or LM Studio — handling confidential drafts and sensitive contracts no longer carries data-egress risk.
For the consumer market: consumer assistants like ChatGPT won't go local anytime soon — cloud APIs are still in aggressive price-cut mode. But privacy-sensitive categories such as therapy and paralegal support will see local-first options emerge.