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Mac mini with M5 Pro and 64 GB
Apple silicon64 GiB307 GB/s9.2 TFLOPS
This machine leaves 61.00 GiB for the model once the system has its share, and it takes 16 of the 20 models we measure at the reference compression. What decides how fast it answers is not the chip: it is the 307 GB/s of memory bandwidth.
Left for the model
Estimated
Memory bandwidth
Vendor declared
Models that fit
Measured by Local AI Scope
Videos: Mac mini with M5 Pro and 64 GB 3
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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.
| Memory | GiB |
|---|---|
| Memory on the box | 64 |
| Reserved for the system | −3.00 |
| Left for the model | 61.00 |
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.
307 GB/s — Vendor declared · checked against the manufacturer’s page on 2026-09-19 (Source).
The 307 GB/s applies to both variants of the M5 Pro and to all three memory sizes: Apple publishes one single figure, unlike the M6 in the same table. What the chip upgrade changes is the GPU: 16 cores as standard and 20 in the variant this sheet is computed from, which is the only one Apple sells with 64 GB fitted. That step moves the estimated reading speed, not what fits.
Compute: 9.2 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.
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.
| 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 | 128k |
| 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 | no | 32k |
| llama4-scout-17b | Q4_K_M |
60.87 GiB | no | no | no | — |
| qwen3.8-flash-next | IQ4_XS |
87.25 GiB | no | no | no | — |
| 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 — 16 of the 20 models measured
- qwen3-1.7b
Q4_K_M— context 32k tokens - qwen3-4b-2507
Q4_K_M— context 256k tokens - gemma4-e2b
Q4_K_M— context 128k tokens - gemma4-e4b
Q4_K_M— context 128k tokens - qwen3-8b
Q4_K_M— context 32k tokens - granite-4.2-8b
Q4_K_M— context 128k tokens - gemma4-12b
Q4_K_M— context 256k tokens - gpt-oss-20b
Q4_K_M— context 128k tokens - mistral-small-24b
Q4_K_M— context 128k tokens - qwen3.8-27b
Q4_K_M— context 256k tokens - qwen3.6-27b
Q4_K_M— context 256k tokens - gemma4-26b-a4b
Q4_K_M— context 256k tokens - gemma4-31b
Q4_K_M— context 128k tokens - qwen3-coder-30b
Q4_K_M— context 256k tokens - qwen3.6-35b-a3b
Q4_K_M— context 256k tokens - gpt-oss-120b
Q4_K_M— context 32k tokens
Models that do not fit — 4 of the 20 models measured
- llama4-scout-17b
Q4_K_M— short by 1.30 GiB - qwen3.8-flash-next
IQ4_XS— short by 27.02 GiB - deepseek-v4-flash
IQ4_XS— short by 67.29 GiB - glm-5.2
Q4_K_M— short by 388.13 GiB
The machines on either side
- Server with 2× RTX 3090 (48 GB) — 47.20 GiB left for the model, 15 / 20 models
- Mac with M4 Max and 64 GB — 61.00 GiB left for the model, 16 / 20 models
- Xiaomi AI Cube, 80 GB — engineering prototype — 77.00 GiB left for the model, 17 / 20 models
- PC with Ryzen AI Max+ 395 and 128 GB unified — 96.00 GiB left for the model, 18 / 20 models
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 $2,899 (United States), as published on 2026-09-23 (Source). Vendor declared
That is the price of this exact configuration: 64 GB with the entry 512 GB drive. The announcement only quotes $1,699, which is the 24 GB base with the 16-core GPU, so the 64 GB this sheet is computed from cost $1,200 more; the same machine with a 1 TB drive is $3,199. Apple publishes no price in euros: this figure is United States dollars.
What we earn on this page
Nothing, today. There is no affiliate link on this page and no commercial agreement behind any figure on it. If a buying link ever appears here, it will be marked as such and the commission declared in this same block. Two rules do not change on that day: the ranking on this site is computed before commercial availability is applied, and «best» never means «pays most».