Choosing the Right LLM for Your Mac: A Practical Guide

There are thousands of open AI models you can download and run for free. That sounds like good news until you actually open the list. Leaderboards rank models nobody's Mac can hold, half the names look like license plates, and the difference between a 8B and a 32B is invisible until you have already bought the wrong hardware.

Here is the honest simplification: for a working local AI setup on Mac, the choice comes down to about a half dozen model families, and which one is right for you is decided by exactly three things. How much RAM your Mac has, how fast it moves memory, and what kind of work you need done. This guide walks through all three, with named models, real speed numbers, and the 12-month cost math against cloud API fees.

We have written before about picking the model class before the hardware. This guide is the companion for the other direction: you have a Mac on your desk, or a configuration in your cart, and you want to know which model to actually put on it.

Why choosing the right LLM beats buying more Mac

The single most expensive mistake in local AI is not underbuying hardware. It is mismatching the model to the work. A law office running a small 8B model on contract review will conclude that local AI is a toy, because at that job it is. A retail shop paying for a 70B-capable machine to write product descriptions is burning $2,000 of capability that a $799 machine delivers identically.

Open models come in size classes, measured in billions of parameters, and the classes are a staircase rather than a ramp. Each step up buys a visible jump in judgment:

• 8B class: drafts emails, summarizes short documents, answers routine questions. Fast, cheap to run, shallow on analysis.
• 12 to 14B class: noticeably better writing, follows multi-step instructions reliably. The sweet spot for most first setups.
• 27 to 32B class: real analysis. Reads a contract and flags what matters, reviews financials, drafts work a professional would sign.
• 70B class: reasoning quality that approaches the big cloud names. For judgment-heavy reading, the difference is not subtle.

The skill is matching the lowest step that does your job well. Every step above that is speed you cannot feel and money you did not need to spend.

RAM requirements for Apple Silicon AI inference

Apple Silicon uses unified memory, one pool of RAM shared by the CPU and GPU, and a model must fit entirely inside it to run at all. Thanks to quantization (compression to roughly 4 bits per parameter with little quality loss), the sizing rule is simple: about 0.6 GB of RAM per billion parameters, plus 6 to 10 GB of headroom for macOS and your everyday apps.

| Model class | File size on disk | Comfortable minimum RAM | |---|---|---| | 8B | ~5 GB | 16 GB | | 12 to 14B | ~8 to 9 GB | 24 GB | | 27 to 32B | ~18 GB | 48 GB | | 70B | ~40 GB | 64 GB |

RAM decides what you can run. Memory bandwidth decides how fast it runs, because during Apple Silicon AI inference the chip rereads the entire model for every single token it generates. The base Mac Mini M4 moves 120 GB/s, the Mini M4 Pro moves 273 GB/s, and the Mac Studio M4 Max moves 410 to 546 GB/s. Divide bandwidth by model file size and you get a rough tokens-per-second ceiling, which is why the same model runs twice as fast on a Pro chip. Our benchmark deep dive has the full tables.

The model menu: what to run on each Mac Mini AI setup

Now the practical part. Here is what we actually install, tier by tier, when we build a Mac Mini AI setup for a client. All models below are free to download and licensed for business use.

Mac Mini M4, 16 GB ($599): Llama 3.1 8B. The dependable workhorse of the 8B class. Expect 20 to 30 tokens per second, which is faster than most people read. A boutique retail shop in Portland uses this tier to turn supplier spec sheets into product descriptions and to draft customer email replies. For that work, an 8B is genuinely enough.

Mac Mini M4, 24 GB ($799): Qwen 14B class or Gemma 12B. The extra $200 of RAM unlocks the class where writing quality visibly improves. Speeds land around 10 to 14 tokens per second on the base chip, still comfortable for conversation. A restaurant group in Austin runs a 14B here for catering quotes, supplier emails, and weekly social captions, and this is the configuration we recommend most often to first-time buyers.

Mac Mini M4 Pro, 48 GB ($1,799): Qwen 32B or Gemma 27B. This is the analysis tier. The Pro chip's 273 GB/s bandwidth moves a 32B at 15 to 22 tokens per second. A dental office in Phoenix uses this class to condense treatment narratives and draft insurance correspondence, with everything processed on the desk, a setup designed for privacy-sensitive workflows. A solo financial advisor summarizing client meetings lives in the same class.

Mac Mini M4 Pro, 64 GB ($1,999): the 70B class, with a caveat. Llama 3.3 70B fits, but at 8 to 11 tokens per second it feels like a slow typist. Fine for background jobs you queue up. Frustrating for live back-and-forth. If 70B is your daily driver, keep reading.

Mac Studio picks for heavier on-device AI for business

The Mac Studio earns its price in exactly two model scenarios for on-device AI for business.

Daily 70B work: Mac Studio M4 Max, 64 GB ($2,499), running Llama 3.3 70B. The Studio's wide memory bus lifts the 70B class to 18 to 22 tokens per second, fully conversational. A two-partner law office in New York doing judgment-heavy contract reading is the textbook case: the 70B catches contractual nuance that a 32B summarizes past, and the whole document set stays on hardware they own.

Long documents and multiple models: Studio with 128 GB (about $3,700). Feeding a model a 40-page agreement means processing roughly 20,000 tokens before the first word of the answer appears, and the Studio's bandwidth turns a minutes-long pause into 15 to 25 seconds. The 128 GB tier also holds several specialized models at once, say a 32B for drafting next to a 70B for review, which is how we structure custom agent setups for teams.

One honest boundary: even a 70B is not the largest frontier cloud model. For day-to-day business work the gap rarely matters, but if a workload genuinely needs cutting-edge reasoning on every request, a hybrid approach where sensitive work stays local is the truthful recommendation, and we will say so in an assessment.

Inference speed: what tokens per second actually feel like

Speed numbers are abstract until you map them to reading pace, so here is the translation. People read at roughly 4 to 5 words per second, and a token is about three quarters of a word.

• 20+ tokens per second: faster than you read. The AI never feels like the bottleneck.
• 10 to 15 tokens per second: a brisk typist. Comfortable for conversation, slightly slow for long outputs.
• Under 8 tokens per second: you will watch it think. Acceptable only for queued background work.

This is why the model-to-hardware match matters twice. A 14B on a base Mini and a 70B on a Studio both land in the comfortable zone. A 70B on a 64 GB Mini technically runs but sits below the comfort line, and that gap is where most local AI disappointment comes from. It is also why we bench-test every configuration on your actual workload before handoff, a step we walk through on our process page.

Total cost of ownership vs cloud API fees

The hardware above is a one-time purchase at Apple retail price, which is exactly what we charge for sourcing. Cloud APIs bill per token forever. Twelve months out, the comparison splits into three honest scenarios.

Light use, cloud stays close. A solo owner running a few drafting tasks a day might spend $30 to 50 a month in API fees, roughly $360 to 600 a year. Against the $799 Mini plus about $15 of electricity for the year (a Mini idles near 4 watts), year one is nearly a wash. The local case at this volume is privacy and year two, not immediate savings, and we will tell you that plainly.

Moderate professional use, breakeven inside the year. A professional pushing 40 to 60 substantial document jobs a week through a 32B-class model commonly generates $150 to 250 a month in API charges, or $1,800 to 3,000 a year. The $1,799 Mini M4 Pro crosses breakeven around month 8 to 12, and every month after that, the marginal cost of the next thousand documents is electricity.

Heavy or team use, breakeven in one to two quarters. A small team can run $400 to 600 a month through an API, $4,800 to 7,200 a year. The $2,499 Studio pays for itself around month 5 to 7. Our month-by-month ledger traces those crossing points in detail.

Hardware is only part of a working system. Setup, model installation, agent configuration, and workload testing are one-time labor on top, and our pricing spells out that number before you commit to anything.

Key Takeaways

• Match the lowest model class that does your job well: 8B drafts, 14B writes, 32B analyzes, 70B reasons. Every step above your need is money wasted.
• RAM decides what fits: about 0.6 GB per billion parameters plus 6 to 10 GB of headroom. 16 GB runs an 8B, 24 GB a 14B, 48 GB a 32B, 64 GB a 70B.
• Bandwidth decides speed: 120 GB/s on the base Mini up to 546 GB/s on the Studio. Aim for 10+ tokens per second on your daily model, 20+ if you chat with it all day.
• The $799 Mini with 24 GB is the right first machine for most owners. Pay Studio prices only for daily 70B work, very long documents, or a shared team server.
• At $150 to 250 a month in API spend, local hardware breaks even inside a year. Below $50 a month, buy for privacy and control rather than savings.

If you would rather skip the leaderboards entirely, this matching exercise is the core of what we do. Maai Machines is a local LLM setup service: we recommend and source the hardware at Apple retail, install and test the right models on your actual workload, configure custom agents, and stay available for support after handoff. And if what you really want is AI-powered marketing done for you rather than a machine on your desk, our sister service MOCO exists for exactly that. Otherwise, visit maaimachines.com or book a free assessment and bring nothing but a description of your work.