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Hardware sheet

Mac Studio with M5 Max and 128 GB

Apple silicon128 GiB614 GB/s18.4 TFLOPS

Mac Studio
Mac Studio Photo: Apple
Mac Studio with LM Studio on screen
Mac Studio with LM Studio on screen Photo: Apple
Apple M5 Max and M5 Ultra chips
Apple M5 Max and M5 Ultra chips Image: Apple
Mac Studio running Draw Things
Mac Studio running Draw Things Photo: Apple

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This machine leaves 125.00 GiB for the model once the system has its share, and it takes 18 of the 20 models we measure at the reference compression. What decides how fast it answers is not the chip: it is the 614 GB/s of memory bandwidth.

At a glanceMachine data · 2026-10-01
Left for the model125.00 GiB
Memory bandwidth614 GB/s
Models that fit18 / 20
Largest model it takesqwen3.8-flash-next
125.00 GiB

Left for the model

Estimated

614 GB/s

Memory bandwidth

Vendor declared

18 / 20

Models that fit

Measured by Local AI Scope

Videos

Videos: Mac Studio with M5 Max and 128 GB 3

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Memory

The memory you can actually use

Unified memory is shared with the operating system and everything you have open. The reserve here is deliberately conservative; you can change it in the tool.

How the memory on the box turns into memory you can use
Memory GiB
Memory on the box 128
Reserved for the system −3.00
Left for the model 125.00
What the Mac Studio with M5 Max and 128 GB really has free for a modelHorizontal bar of the 128 GiB of the Mac Studio with M5 Max and 128 GB, split into 3.0 GiB for the system, 87.2 GiB for the qwen3.8-flash-next model, 0.9 GiB for the context and 36.8 GiB free.0128 GiBSystem: 3.0 GiBSystem 3.0Model: 87.2 GiBModel87.2Context (8,192): 0.9 GiBContext (8,192) 0.9Headroom: 36.8 GiBHeadroom36.8
Of the 128 GiB the machine has, 3.0 GiB never reach the model. The bar shows qwen3.8-flash-next at IQ4_XS, the largest of the 20 models in the dataset that still fits at a 8,192-token context, and the KV cache that context needs — computed layer by layer, not with the usual rule of thumb.
Speed

Memory bandwidth

Two different limits, and people confuse them constantly. While the model is writing its answer, bandwidth rules: every single token means reading the weights again, so tokens per second is roughly bandwidth divided by model size. While the model is reading your document, compute rules: the whole input is processed at once. That is why a laptop with no GPU can take minutes before the first word appears and then type at a tolerable pace.

614 GB/s — Vendor declared · checked against the manufacturer’s page on 2026-09-18 (Source).

The 614 GB/s belongs to the 40-core GPU variant of the M5 Max, which is the only one sold with 128 GB. The standard configuration is a different chip: 36 GB, a 32-core GPU and 460 GB/s. Apple’s announcement only says «up to», so the number that matters depends on which of the two you buy.

Compute: 18.4 TFLOPS (estimate for this class of machine) — Estimated · the manufacturer does not publish this figure at all; ours is a conservative estimate, not a specification. The figures in this line do not all come from the same measure across machines, because each manufacturer publishes a different one. Compare them between machines only with that in mind.

Fit

Which models fit here, and which do not

Fitting means three things at once fit in the memory left over: the model file, its context memory, and the runtime’s working headroom. Context memory is computed from each model’s declared attention pattern layer by layer, not from the generic formula — which is why several models fit here that other calculators say do not.

Every measured model against this machine: size, whether it fits at each context, and the largest context it holds
Model Compression shown Model size 8k 32k 128k Largest context that fits
qwen3-1.7b Q4_K_M 1.03 GiB yes yes — 32k
qwen3-4b-2507 Q4_K_M 2.33 GiB yes yes yes 256k
gemma4-e2b Q4_K_M 2.89 GiB yes yes yes 128k
gemma4-e4b Q4_K_M 4.63 GiB yes yes yes 128k
qwen3-8b Q4_K_M 4.68 GiB yes yes — 32k
granite-4.2-8b Q4_K_M 4.98 GiB yes yes yes 128k
gemma4-12b Q4_K_M 6.63 GiB yes yes yes 256k
gpt-oss-20b Q4_K_M 10.83 GiB yes yes yes 128k
mistral-small-24b Q4_K_M 13.35 GiB yes yes yes 128k
qwen3.8-27b Q4_K_M 15.33 GiB yes yes yes 256k
qwen3.6-27b Q4_K_M 15.66 GiB yes yes yes 256k
gemma4-26b-a4b Q4_K_M 15.78 GiB yes yes yes 256k
gemma4-31b Q4_K_M 17.07 GiB yes yes yes 256k
qwen3-coder-30b Q4_K_M 17.28 GiB yes yes yes 256k
qwen3.6-35b-a3b Q4_K_M 20.61 GiB yes yes yes 256k
gpt-oss-120b Q4_K_M 58.46 GiB yes yes yes 128k
llama4-scout-17b Q4_K_M 60.87 GiB yes yes yes 256k
qwen3.8-flash-next IQ4_XS 87.25 GiB yes yes yes 256k
deepseek-v4-flash IQ4_XS 127.28 GiB no no no —
glm-5.2 Q4_K_M 433.83 GiB no no no —

Sizes are the real published files at the reference compression, one step per row: Q4_K_M where the author publishes it, the nearest neighbour where they do not. Every row states which one it is showing. Q4_K_M is our quality floor: below it the loss is audible in the answers. A model that would only fit here at a harsher compression is listed as not fitting, on purpose. A dash in a context column means the model itself does not offer that context, so there is nothing to fit.

Models that fit — 18 of the 20 models measured

Models that do not fit — 2 of the 20 models measured

The machines on either side

Price

Price and availability

What a machine costs decides as much as what fits inside it, so the price belongs on this page. What follows is only what the manufacturer publishes, with the market it applies to and the day it was read.

From $5,099 (United States), as published on 2026-09-23 (Source). Vendor declared

That is the price of this exact configuration: 128 GB with the entry 512 GB drive. The announcement only quotes $2,499, which is the 36 GB base — a different chip, with a 32-core GPU and 460 GB/s — so the 128 GB this sheet is computed from cost $2,600 more; with a 1 TB drive it is $5,399. Apple publishes no price in euros: this figure is United States dollars.

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