Mac mini vs Mac Studio for local AI

I build Outlier, a local-AI app for Apple Silicon, so I have an obvious interest in you buying one of these. I'll try to be useful anyway — including the part where a PC with a discrete GPU is the better purchase. Every spec and price below came off Apple's own pages on 23 July 2026.

Capacity and bandwidth are two different problems

Running a model locally makes two demands. Capacity: the weights, the KV cache and the OS all have to fit in memory, or you wait on the SSD. Bandwidth: generation reads a large fraction of those weights for every token, so decoding is memory-bound and bandwidth predicts tokens per second far better than GPU core count does.

Apple silicon is good at the first. CPU and GPU share one pool — no separate VRAM budget, no host-to-device copy — and Apple's MLX framework is built on that assumption. Whatever RAM you bought is GPU memory. On the second, the spread is enormous: 120GB/s on M4, 273GB/s on M4 Pro, 410GB/s on M4 Max (546GB/s with the 16-core CPU / 40-core GPU chip), 819GB/s on M3 Ultra. Seven-to-one, across two lines that look alike on a spec sheet.

What each configuration actually fits

Gigabytes are hard to reason about in the abstract, so here are measured numbers from Outlier's tier table — the example I know best. Weights are 4-bit; RAM figures are minimums including headroom for context and the OS.

• 16GB (base M4 mini): a 4B model is a 2.37GB download needing 6GB of RAM; a 9B model is 5.04GB needing 12GB. Both comfortable. A 26B MoE model at 15.61GB has a 16GB minimum — it runs, but that's the floor.
• 24GB (M4 mini upgraded, M4 Pro mini base): dense 27B models become practical — 15.13GB on disk, about 20.7 tok/s on an M1 Ultra. A 35B MoE vision model at 19.0GB also lands. The sweet spot for most people.
• 64GB (M4 Max Studio, 16/40 chip only): the floor for very large paged-MoE models. Outlier's 397B-a17b tier is a 209GB download that streams experts from SSD — about 2.1 tok/s, a batch speed rather than a conversational one.
• 96GB (M3 Ultra Studio): the same models with headroom, at 819GB/s instead of 410.

Bandwidth decides how pleasant that is. On my M1 Ultra a 4B model measures 71.7 tok/s; the same model measures 32 tok/s on an M4 MacBook Air. An M4 mini will run a 27B model — at roughly reading speed rather than faster than you read.

Side by side

Thermals, noise, and sustained load

Inference is a sustained load, not a burst. Chatting produces spikes, but an agent loop or an overnight index produces hours of near-continuous GPU work, and thermal envelope matters more under that pattern than its headline number suggests.

Apple rates maximum continuous power at 155W for the mini and 480W for the Studio, and lists the mini at 5 dBA idle. Loaded, you hear it. The mini will run models all day, but it's a small box with a small fan — which is why I'd steer sustained work to a Studio even when the mini's memory fits the model. Both still run off a normal wall outlet.

Price per usable gigabyte — and the June price rise

Divide starting price by base memory and the lineup isn't linear: roughly $50/GB for the $799 M4 mini, $67 for the $1,599 M4 Pro mini, $69 for the $2,499 M4 Max Studio, and — despite the sticker — about $55/GB for the $5,299 M3 Ultra, which also has the bandwidth to use those gigabytes. The middle is the expensive part.

These numbers got worse this year. Apple's education price list carries a pricing date of 25 June 2026, dating a change: the mini started at $599 in October 2024, the Studio at $1,999 in March 2025. Ceilings fell at the same time. Apple's March 2025 announcement described configuring M3 Ultra up to 512GB and running models with over 600 billion parameters on device; the 128GB, 256GB and 512GB options are gone, as is the 64GB M4 Pro mini. Third-party reporting (MacRumors, May 2026) blames the DRAM shortage — I couldn't confirm that from a primary Apple source, only the resulting lineup.

Upgrade paths: there aren't any

Unified memory is configure-to-order with no user-serviceable path, so the ceiling is fixed at checkout and "I'll add RAM later" isn't a plan. What you can add is storage over Thunderbolt 5 at up to 120Gb/s — four ports on the Studio, three on the M4 Pro mini, while the base M4 mini is Thunderbolt 4 — plus optional 10Gb Ethernet. Fast external storage helps models that stream weights from disk; it's no substitute for memory.

Both desktops are also a silicon generation behind. Apple shipped M5 Pro (up to 307GB/s, 64GB) and M5 Max (up to 614GB/s, 128GB) in the MacBook Pro in March 2026, claiming over 4× the peak GPU compute of M4 Pro for AI. Neither is in a desktop — so you can order a MacBook Pro with 128GB today and no Mac Studio above 96GB.

Where these machines genuinely win — and where they don't

Second half first, because it's the part vendors skip. On raw throughput, a PC with a discrete GPU is usually faster. Dedicated graphics memory runs at bandwidths these desktops don't reach, and the CUDA stack is deeper and better optimized for training and batch serving. For any model that fits inside a card's VRAM, expect more tokens per second there — so if your model fits in 24 or 32GB and speed is all you're buying, buy that instead.

The Apple case is capacity per dollar and power draw, and it's strong:

• Unified memory removes the VRAM cliff. Getting 96GB of GPU-addressable memory out of discrete cards means stacking them, with the power supply, chassis and noise that implies. Here it's one box on a desk.
• 819GB/s inside a 480W envelope has no direct consumer-PC equivalent at this size or noise level.
• Everything is capital cost — no per-token billing, no rate limits, no egress, and it works with the network off.
• They're normal computers: AppleCare, service, resale, fully useful for everything else.
• The $799 M4 mini is a real entry point — 16GB standard, a first-party ML stack in MLX, Metal and Core ML, and good third-party support across llama.cpp, Ollama and LM Studio.

One caveat for both: the hardware supplies no model, no license and no privacy guarantee — those come from the software you install. Apple Intelligence itself is hybrid, mixing on-device processing with Private Cloud Compute, so "it's a Mac" isn't by itself an on-device claim. Check the app, not the box.

Who should pick which

The $799 M4 Mac mini if you're finding out whether local AI fits your work. Cheapest honest test: 4B and 9B models run briskly, a 26B MoE model sits at the memory floor, and if local isn't for you, you still own a good small computer.

The M4 Pro Mac mini (24–48GB) if you know you want a 27B-class daily driver, quietly and cheaply. 273GB/s is a real step up from 120, at less than half a Studio's entry price.

The M4 Max Mac Studio if the machine will be under sustained load — agent loops, batch jobs, video work alongside model work — or if you need 64GB, which requires the 16-core CPU / 40-core GPU configuration at $3,499.

The M3 Ultra Mac Studio only if 96GB at 819GB/s is the actual requirement; you're buying bandwidth on a chip announced in March 2025. If your models fit in 48GB this isn't your machine, and if you needed 256GB, the machine you wanted isn't for sale.

A PC with a discrete GPU if your model fits in VRAM, throughput is the goal, and you'll take on the power, noise and assembly.

Either way, test the premise on the Mac you already own before spending anything.