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Mac mini with M6 and 32 GB
Apple silicon32 GiB170 GB/s5.5 TFLOPS
Once the system takes its share, this machine leaves 29.00 GiB for the model: room for 19 of the 26 models we measure at the reference compression, and the heaviest one that fits is nemotron-3.5-lightning-30b. And how fast does it answer? That’s not down to the chip, but to its 170 GB/s of memory bandwidth.
Left for the model
Estimated
Memory bandwidth
Vendor declared
Models that fit
Measured by Local AI Scope
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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 | 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 they are easy to mix up. While the model is writing its answer, bandwidth rules: every token means reading the weights it uses again — all of them in a dense model; in a mixture of experts, the shared layers plus only the active experts — so tokens per second is roughly bandwidth divided by that amount. 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 the generic formula would rule out.
| 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 |
| granite-4.2-8b | Q4_K_M |
4.98 GiB | yes | yes | yes | 128k |
| qwen3.5-9b | Q4_K_M |
5.29 GiB | yes | yes | yes | 256k |
| 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 |
| muse-glimmer-30b | Q4_K_M |
15.61 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 | 256k |
| gemma4-31b | Q4_K_M |
17.07 GiB | yes | yes | no | 64k |
| 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 |
| flint-40b | Q4_K_M |
23.61 GiB | yes | yes | no | 64k |
| nemotron-3.5-lightning-30b | Q4_K_M |
23.73 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 | — |
| nemotron-3.5-super-120b | Q4_K_M |
65.29 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-5.3-flash | Q4_K_M |
187.04 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 — 19 of the 26 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 - granite-4.2-8b
Q4_K_M— context 128k tokens - qwen3.5-9b
Q4_K_M— context 256k 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 - muse-glimmer-30b
Q4_K_M— context 128k tokens - qwen3.6-27b
Q4_K_M— context 128k tokens - gemma4-26b-a4b
Q4_K_M— context 256k tokens - gemma4-31b
Q4_K_M— context 64k tokens - qwen3-coder-30b
Q4_K_M— context 64k tokens - qwen3.6-35b-a3b
Q4_K_M— context 128k tokens - flint-40b
Q4_K_M— context 64k tokens - nemotron-3.5-lightning-30b
Q4_K_M— context 128k tokens
Models that do not fit — 7 of the 26 models measured
- gpt-oss-120b
Q4_K_M— short by 30.28 GiB - llama4-scout-17b
Q4_K_M— short by 33.30 GiB - nemotron-3.5-super-120b
Q4_K_M— short by 37.00 GiB - qwen3.8-flash-next
IQ4_XS— short by 59.02 GiB - deepseek-v4-flash
IQ4_XS— short by 99.29 GiB - glm-5.3-flash
Q4_K_M— short by 158.80 GiB - glm-5.2
Q4_K_M— short by 406.12 GiB
The machines on either side
- Mac with M4 Pro and 24 GB — 21.00 GiB left for the model, 16 / 26 models
- Workstation with an RTX 4090 24 GB — 23.20 GiB left for the model, 17 / 26 models
- Mini-PC with an NPU and 32 GB LPDDR5X — 29.00 GiB left for the model, 19 / 26 models
- Server with 2× RTX 3090 (48 GB) — 47.20 GiB left for the model, 19 / 26 models
The same machine with different memory
- Mac mini with M6 and 16 GB — 13.00 GiB left for the model, 9 / 26 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 $1,299 (United States), as published on 2026-09-23 (Source). Vendor declared
That is the price of this exact configuration: 32 GB with the entry 256 GB drive. The press release only quotes $899, which is the 16 GB one, so the 32 GB cost $400 more; the 24 GB step sits at $1,099. Apple does not publish a price in euros: the figure is in US 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».