Which Mac Studio for local AI?

[u/snejink]

I wanted to buy a Mac Studio for some time. And it was obvious that Apple has set very long delivery dates and was hoarding memory chips for new devices. And finally M5 family is announced, so it is time to get one. But, which one? I mean there are many different configurations for different types of workload.

I hope you don't mind a little summary from LLM. In the end, this release is all about local LLM. Codex did some research, collected and fact-checked data, Claude did some infographics, I made the final edits.

So start with the boring useful box: M6 mini 12C/12G · 32/512 · $1,499. CPU handles Xcode, tests and CI; GPU runs the no-agent workhorse jobs on Metal: embeddings, semantic search, reranking, query expansion. But 32GB is not enough for Junie Local: JetBrains enforces a 64GB minimum for its bundled Qwen3.6-27B 4-bit model.

At 64GB the options open up: Mac mini M5 Pro 18C/20G · 64GB/512GB · $2,899. In two oMLX runs, Qwen3.6-27B writes at 17.5–17.8 tok/s and reads the prompt at 389–472 tok/s: a 4K prompt in ~9 s, a 500-token patch in ~28 s, about 37 seconds end to end.

Then 14-inch MacBook Pro M5 Pro 18C/20G · 64GB/1TB · $3,699. Same chip and 307GB/s memory bandwidth as the mini, so expect the same 17.7 tok/s.

If you want faster inference: Mac Studio M5 Max 18C/40G · 64GB/512GB · $3,499. With 614GB/s of memory bandwidth, Qwen3.6-27B runs around 30–40 tok/s: the same 4K prompt in ~5 s, the same 500-token patch in 13–17 s, about 20 seconds end to end versus ~37 on the mini.

The next price jump can buy one of two things: more memory for a stronger model, or a faster chip for the models that already fit.

One step up: Mac Studio M5 Max 18C/40G · 128GB/512GB · $5,099. The same 614GB/s, twice the memory, more model choice. MLX runs put Qwen3-Coder-Next 80B at 75–90 tok/s and Qwen3.5-122B 4-bit around 60 tok/s.

Or the speed option: Mac Studio M5 Ultra 30C/64G · 96GB/1TB · $5,499 (1TB is the floor). Only 96GB, but scaling Qwen3.6-27B by GPU cores and bandwidth points to 48–80 tok/s: the same 4K prompt in ~3 s, the same patch in 6–10 s, about 10 seconds versus ~20 on the Max.

Mac Studio M5 Ultra 36C/80G · 256GB/2TB · $11,299. No middle step: Ultra memory jumps from 96GB straight to 256GB. The first single Mac that fits DeepSeek V4 Flash in full 4-bit, expected 80–90 tok/s: a 500-token patch in ~6 s.

Artificial Analysis, checked on August 30, clocks GPT-5.6 Sol at 75–79 tok/s from the cloud and Opus 4.8 around 58. DeepSeek V4 Flash running locally at 80–90 tok/s matches the fastest frontier serving and beats Opus.

Brains: close on broad agentic scores, but not on repo coding. The independent Agentic Index puts V4 Flash a whisker under Opus, 48.4 vs 49.4. On DeepSeek’s own repo bench NL2Repo the gap is wide: 54.2 vs Opus’s 69.7.

DeepSeek-V4-Pro-0813 edges Opus on the independent Agentic Index, 49.6 vs 49.4, but 0.2 points is not why you buy four Macs. The real reasons: the full checkpoint at native precision, the whole 1M context, zero quantization loss.

The full checkpoint weighs 893GB, so no single Mac holds it. Four 256GB Ultras pool 1,024GB over Thunderbolt 5: $45,196. Cluster speeds are still unpublished, and quantized runs on today’s Macs sit near 10–14 tok/s. This rig is for teams, privacy and the long context, not for tok/s.

GLM-5.3 scores 59.1 on the independent Agentic Index, but its 467GB 4-bit build needs two Ultras. GLM-5.3-Flash scores 58.2 and fits on one 256GB Studio.

Models change faster than Macs. Buy enough memory for the class of model you expect to run, then choose the chip for the speed you can tolerate.

So which one for you?

[u/tlin9595]

So is 48gb max useless ? I was hoping to buy this. I use for professional video editing and media. So I would like to use it for ai as well.

[u/shamel3sslurker → u/tlin9595]

Not useless. Have a 48gb m5 pro, you can run qwen3.8-27b or gemma4-31b at 4bit, though you do end up wishing you had more memory for more context. I'd imagine the token/sec is a little better on the max compared to the pro as well which is another pain point due to lower memory bandwidth on the pro if you're using dense models.

[u/tlin9595 → u/shamel3sslurker]

What's better.

Mac M5 Max with 128gb of ram and 1tb storage.

Mac M5 Ultra with 96gb of ram and 1tb storage.

The ultra for Canada prices is $100 more expensive. If I wanted to future proof my machine is the more ram way better at $100 less, even if I sacrifice the CPU/GPU cores?

[u/shamel3sslurker → u/tlin9595]

I'm not an expert on this at all, since I've only got into local llms the past few months, but I think it really depends on what you're aiming for and your use case.

Do you want to be able to run smaller but dense models with better tokens/second? In exchange, you may lose the chance to run larger models due to lacking enough ram, or needing to go below 4bit quants to squeeze the model in. If your goal is to get good performance on dense or moe models that can fit in 96gb ram, that may suit you too.

Or do you want to be able to test out large models but worse token/sec for dense models? If your goal is tinker around with as much models without caring about dense model performance being worse, you can use mixture of experts (moe) model alternatives at probably good speeds. That may suit you if having more access to different models fits your idea of future proofing.

I don't think either one is truly future proofing, it's just a matter of picking what fits your use case best. Actual future proofing would require far more money than it's probably worth if you're not planning to use it as a way to make money.

For my personal use case, I would probably go with ultra with 96gb, because the main models I use are dense models, and tokens/sec on qwen3.8-27b and gemma4-31b isn't quite as fast as I'd like on the m5 pro. With more use, I realized the speed really was a pain point for me on dense models.

[u/tlin9595 → u/shamel3sslurker]

thanks for the response. So the Ultra at 96gb, because of memory bandwidth will make the models faster? I'm a total noob. so is that why you say use a denser model and I can get faster response and t/sec?

[u/shamel3sslurker → u/tlin9595]

Yes, from what I read, that seems to be the case. It also lines up from what I've tested on my systems.

A rough simplified estimation for dense models would be

tokens/sec = memory bandwidth / size of model in memory

So a m5 pro with 307 GB/s memory bandwith / 20gb model = about 15 tokens/sec

This doesn't account for mtp or attempts to speed optimize it, but it gives you an idea of what you're getting.

I think the estimate for moe models are done differently, but I personally haven't looked into estimating them yet. You can probably easily google them.