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Mac with M4 Pro and 24 GB
Apple silicon24 GiB273 GB/s9.2 TFLOPS
The manufacturer no longer sells this machine new. It stays in this hub because the arithmetic still answers the question if you are looking at a second-hand unit or comparing it with what is on sale today; the store no longer lists any new machine with this chip. Which one replaces it, and at what price, is in the price block further down.
Once the system takes its share, this machine leaves 21.00 GiB for the model: room for 16 of the 26 models we measure at the reference compression, and the heaviest one that fits is qwen3-coder-30b. And how fast does it answer? That’s not down to the chip, but to its 273 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 | 24 |
| Reserved for the system | −3.00 |
| Left for the model | 21.00 |
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.
273 GB/s — Vendor declared · checked against the manufacturer’s page on 2026-08-25 (Source).
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 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 | no | 64k |
| 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 | no | 64k |
| 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 | 32k |
| qwen3.8-27b | Q4_K_M |
15.33 GiB | yes | yes | no | 32k |
| muse-glimmer-30b | Q4_K_M |
15.61 GiB | yes | yes | yes | 128k |
| qwen3.6-27b | Q4_K_M |
15.66 GiB | yes | yes | no | 32k |
| gemma4-26b-a4b | Q4_K_M |
15.78 GiB | yes | yes | no | 64k |
| gemma4-31b | Q4_K_M |
17.07 GiB | yes | no | no | 16k |
| qwen3-coder-30b | Q4_K_M |
17.28 GiB | yes | no | no | 16k |
| qwen3.6-35b-a3b | Q4_K_M |
20.61 GiB | no | no | no | — |
| flint-40b | Q4_K_M |
23.61 GiB | no | no | no | — |
| nemotron-3.5-lightning-30b | Q4_K_M |
23.73 GiB | no | no | no | — |
| 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 — 16 of the 26 models measured
- qwen3-1.7b
Q4_K_M— context 32k tokens - qwen3-4b-2507
Q4_K_M— context 64k 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 64k 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 32k tokens - qwen3.8-27b
Q4_K_M— context 32k tokens - muse-glimmer-30b
Q4_K_M— context 128k tokens - qwen3.6-27b
Q4_K_M— context 32k tokens - gemma4-26b-a4b
Q4_K_M— context 64k tokens - gemma4-31b
Q4_K_M— context 16k tokens - qwen3-coder-30b
Q4_K_M— context 16k tokens
Models that do not fit — 10 of the 26 models measured
- qwen3.6-35b-a3b
Q4_K_M— short by 0.37 GiB - flint-40b
Q4_K_M— short by 3.42 GiB - nemotron-3.5-lightning-30b
Q4_K_M— short by 3.43 GiB - gpt-oss-120b
Q4_K_M— short by 38.28 GiB - llama4-scout-17b
Q4_K_M— short by 41.30 GiB - nemotron-3.5-super-120b
Q4_K_M— short by 45.00 GiB - qwen3.8-flash-next
IQ4_XS— short by 67.02 GiB - deepseek-v4-flash
IQ4_XS— short by 107.29 GiB - glm-5.3-flash
Q4_K_M— short by 166.80 GiB - glm-5.2
Q4_K_M— short by 414.12 GiB
Willing to compress a bit more? Then not everything on this list is out of reach. In the 21.00 GiB of unified memory left for the model, qwen3.6-35b-a3b runs at Q4_K_S (19.46 GiB) with up to 16k tokens of context; flint-40b runs at Q3_K_M (18.19 GiB) with up to 32k tokens of context, and nemotron-3.5-lightning-30b runs at IQ4_XS (17.62 GiB) with up to 64k tokens of context. Below the compression in the table a model loses quality, and at Q3_K_M noticeably more; that is why our count sticks to the table.
The machines on either side
- Mac mini with M6 and 16 GB — 13.00 GiB left for the model, 9 / 26 models
- Office laptop, CPU only, 16 GB — 13.00 GiB left for the model, 9 / 26 models
- Workstation with an RTX 4090 24 GB — 23.20 GiB left for the model, 17 / 26 models
- Mac mini with M6 and 32 GB — 29.00 GiB left for the model, 19 / 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.
There is no price: this row is a class of machine, not a single product with a price list.
Apple no longer sells any new Mac with an M4 Pro: as of 19 September 2026 the store offers M6 and M5 Pro in the Mac mini, and M5, M5 Pro and M5 Max in the MacBook Pro. So there is no current manufacturer price for this class — the closest machine on sale with more than 24 GB within reach is the Mac mini with M5 Pro, from $1,699.
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».