The Mac Studio occupies a specific niche in local AI: it is the machine you buy when you want to run models that won't fit on a laptop. Apple's unified memory architecture means the GPU can address the entire memory pool, so a 64 GB or 128 GB Mac Studio can load a 70B-parameter model that would otherwise demand a multi-GPU PC rig costing far more and drawing far more power. This guide reports our own Mac Studio tests — how much memory translates into how big a model, the tokens per second you actually get by model size, and how long large models take to load.
Want the short version? Jump to the summary table. Most people don't need a Mac Studio at all — PocketLLM runs capable models on your iPhone with zero telemetry. Join the waitlist.
The Mac Studio's whole pitch is large unified memory. Budget about 70% of RAM for weights: a 64 GB Studio runs 70B at Q4 (~40 GB), 128 GB runs 70B at higher quality or a quantized 100B, and 256 GB+ Ultra configs load 180B-class models. Speed is bandwidth-bound — Max chips do ~8-12 tok/s on a 70B, Ultra chips ~15-20. If you only need 3B-13B models, a Mac mini or even a phone is the smarter buy.
How we tested
- Runtime: llama.cpp with Metal, plus MLX for cross-checks, using Q4_K_M GGUF weights throughout. Same quantization across all model sizes so the comparison is clean.
- Models: A ladder from 7B up to 70B and a 180B-class model, so we could see where each memory tier hits its ceiling.
- Metrics: Tokens per second at 512-token generation, peak resident memory, and cold load time from the internal SSD.
- Configs: Mac Studio Max-tier and Ultra-tier, with memory configurations from 64 GB up. The Ultra's roughly doubled memory bandwidth is the variable that matters most at large model sizes.
The headline takeaway from every run: on Apple Silicon, generation speed scales with memory bandwidth, while the maximum model size you can load scales with total memory. The Mac Studio gives you a lot of both, which is exactly why it can run models a MacBook cannot. For the smaller-machine end of the lineup, see our Mac mini local LLM tests.
Memory: how big a model fits
64 GB — the 70B entry point
A 64 GB Mac Studio is the practical entry point for 70B-class models. A 70B at Q4 is roughly 40 GB on disk and peaks a little above that in memory once you add context, leaving comfortable headroom for the OS. This is the config most people who "want to run the big open models locally" should start with. It also flies on anything smaller — 7B, 13B, and 30B models leave most of the memory idle.
128 GB — quality headroom
With 128 GB you stop having to compromise on quantization. You can run a 70B at Q6 or Q8 for better output quality, hold a very long context window, or load a quantized 100B-class model. This tier is the sweet spot for people who run large models daily and care about output quality over running the absolute largest weights possible.
256 GB and up — the frontier-at-home tier
The Ultra configurations with 256 GB or more are where you can load 180B-class models at Q4 (~100 GB) and still leave room to work, or experiment with 400B-class mixture-of-experts models at aggressive quantization. This is overkill for almost everyone, but if your goal is running the largest open weights locally without a server rack, this is the only single-box consumer machine that does it gracefully.
Speed: tokens per second by model size
Across our runs, the pattern was consistent. Small models are fast everywhere; large models separate the Max from the Ultra. On a Max-tier Studio we measured roughly 50-60 tok/s for a 7B, 30-35 tok/s for a 13B, and 8-12 tok/s for a 70B. The Ultra's wider memory bus lifted the 70B into the 15-20 tok/s range — a meaningful difference when you're reading long generations. At 7B and 13B, the chip tier barely matters because you're nowhere near the bandwidth ceiling.
Load time
Load time is an SSD story, not a chip story. Reading weights into memory is sequential disk I/O, so a 7B (~4.5 GB) loads in a few seconds, a 70B (~40 GB) takes roughly 20-40 seconds on a cold first load, and a 180B-class model can take a minute or more. After the first load, macOS keeps much of the file in its unified buffer cache, so reloading the same model is markedly faster. If you switch between several large models, expect to pay the cold-load cost each time you exceed the cache.
The summary table
| Model (Q4) | Size on disk | Fits in | Speed (Max) | Speed (Ultra) |
|---|---|---|---|---|
| 7B | 4.5 GB | Any Studio | 50-60 tok/s | 55-65 tok/s |
| 13B | 8 GB | Any Studio | 30-35 tok/s | 35-40 tok/s |
| 30B | 18 GB | 32 GB+ | 18-22 tok/s | 24-28 tok/s |
| 70B | 40 GB | 64 GB+ | 8-12 tok/s | 15-20 tok/s |
| 180B | ~100 GB | 256 GB+ | n/a | 5-8 tok/s |
Which Mac Studio should you buy?
If your largest target is 70B: a 64 GB Max-tier Studio is the value pick. It runs everything up to 70B at Q4 with usable speed.
If you run 70B daily and want quality: step up to 128 GB so you can run higher-precision quantizations and long context without juggling.
If you want to load the largest open weights: a 256 GB+ Ultra is the only consumer single-box option that handles 180B-class models gracefully. For a full buying guide across the Mac lineup, see our best Mac for local LLM roundup.
Do you actually need this?
Be honest about your workload. The models most people use day to day — for chat, drafting, summarizing, and coding help — are 3B to 13B, and those run beautifully on a Mac mini, a MacBook, or even a phone. The Mac Studio earns its price only when you specifically need 70B-and-larger models. If you're choosing what to actually run rather than what hardware to buy, our best local LLM models ranking is the better starting point. And for everyday on-the-go use, PocketLLM runs models like Llama 3.2 3B fully on your iPhone — no Studio required.
Frequently asked questions
How big a local LLM can a Mac Studio run?
It depends on the unified memory. As a rule of thumb you can dedicate roughly 70% of unified memory to model weights and leave the rest for the OS and context. A 64 GB Mac Studio comfortably runs 70B models at Q4 (~40 GB), a 128 GB config runs 70B at higher quality or a quantized 100B-class model, and a 256 GB+ Ultra config can load very large models like a 180B at Q4 or experiment with 400B-class models at aggressive quantization. The Mac Studio's advantage over laptops is precisely this large, fast unified-memory pool.
How many tokens per second does a Mac Studio get on local LLMs?
In our testing, a Mac Studio M-series Max ran a 7B model at Q4 around 50-60 tok/s, a 13B around 30-35 tok/s, and a 70B around 8-12 tok/s. The Ultra chips, with roughly double the memory bandwidth, push the 70B into the 15-20 tok/s range. Generation speed on Apple Silicon is bandwidth-bound, so the Ultra's wider memory bus is what separates it from the Max once models get large. Smaller models are fast on any Mac Studio.
Is a Mac Studio worth it for local LLMs?
If you want to run 70B-and-larger models locally at usable speed, a Mac Studio is one of the most cost- and power-efficient ways to do it, because Apple's unified memory lets you load models that would need multiple expensive GPUs on a PC. For 3B-13B models, a Mac mini or MacBook is plenty and far cheaper. The Mac Studio earns its price specifically when you need large unified memory — 64 GB and up — to hold big models that won't fit elsewhere without a multi-GPU rig.
How long does a large model take to load on a Mac Studio?
Load time is dominated by reading the weights off the SSD into memory. In our tests a 7B Q4 model loaded in a few seconds, a 70B Q4 (~40 GB) took roughly 20-40 seconds on the first cold load, and very large 180B-class models took a minute or more. After the first load, macOS keeps much of the file cached, so subsequent loads are faster. A fast internal SSD matters more for load time than the chip tier does.
Do I need a Mac Studio, or will an iPhone or Mac mini do?
For most people, no. A phone runs 1B-3B models, an iPad Pro or Mac mini comfortably runs 3B-13B, and that covers everyday chat, drafting, and coding help. You only need a Mac Studio if you specifically want to run 70B-or-larger models locally. PocketLLM is built for the phone and tablet end of that range — it runs models like Llama 3.2 3B fully on-device, with no account and zero telemetry, which is enough for most day-to-day use.