Home›Hardware›Mac mini with M6 and 32 GB
Mac mini with M6 and 32 GB
Apple silicon32 GiB170 GB/s5.5 TFLOPS
This machine leaves 29.00 GiB for the model once the system has its share, and it takes 14 of the 18 models we measure at the reference compression. What decides how fast it answers is not the chip: it is the 170 GB/s of memory bandwidth.
Left for the model
Estimated
Memory bandwidth
Vendor declared
Models that fit
Measured by Local AI Scope
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 | 32 |
| Reserved for the system | −3.00 |
| Left for the model | 29.00 |
There is no Mac mini with M6 and 64 GB. Apple’s own announcement says 16 GB of standard unified memory, configurable up to 32 GB. The 64 GB configuration belongs to the other Mac mini announced the same day, the one with M5 Pro, which is a different chip with a different ceiling. That ceiling is what decides whether a model fits at all, so it is the most important number on this page — and it is 32.
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.
170 GB/s — Vendor declared · checked against the manufacturer’s page on 2026-08-27 (Source).
The 170 GB/s belongs to the M6. Apple announced a second Mac mini the same day, with M5 Pro: a different chip at 307 GB/s and up to 64 GB, from $1,699. And Apple calls the M6 its «first state-of-the-art 2-nanometer chip» without naming a foundry: the specific foundry process repeated in the coverage is not Apple’s word, so it is not on this page either.
Compute: 5.5 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 | 128k |
| 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 |
| 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 | no | 64k |
| qwen3.8-27b | Q4_K_M |
15.33 GiB | yes | yes | yes | 128k |
| qwen3.6-27b | Q4_K_M |
15.66 GiB | yes | yes | yes | 128k |
| gemma4-26b-a4b | Q4_K_M |
15.78 GiB | yes | yes | yes | 128k |
| gemma4-31b | Q4_K_M |
17.07 GiB | yes | yes | no | 32k |
| qwen3-coder-30b | Q4_K_M |
17.28 GiB | yes | yes | no | 64k |
| qwen3.6-35b-a3b | Q4_K_M |
20.61 GiB | yes | yes | yes | 128k |
| gpt-oss-120b | Q4_K_M |
58.46 GiB | no | no | no | — |
| llama4-scout-17b | Q4_K_M |
60.87 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 — 14 of the 18 models measured
- qwen3-1.7b
Q4_K_M— context 32k tokens - qwen3-4b-2507
Q4_K_M— context 128k 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 - gemma4-12b
Q4_K_M— context 256k tokens - gpt-oss-20b
Q4_K_M— context 128k tokens - mistral-small-24b
Q4_K_M— context 64k tokens - qwen3.8-27b
Q4_K_M— context 128k tokens - qwen3.6-27b
Q4_K_M— context 128k tokens - gemma4-26b-a4b
Q4_K_M— context 128k tokens - gemma4-31b
Q4_K_M— context 32k tokens - qwen3-coder-30b
Q4_K_M— context 64k tokens - qwen3.6-35b-a3b
Q4_K_M— context 128k tokens
Models that do not fit — 4 of the 18 models measured
- gpt-oss-120b
Q4_K_M— short by 30.28 GiB - llama4-scout-17b
Q4_K_M— short by 33.30 GiB - deepseek-v4-flash
IQ4_XS— short by 99.29 GiB - glm-5.2
Q4_K_M— short by 420.13 GiB