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Office laptop, CPU only, 16 GB
CPU only16 GiB83 GB/s1.2 TFLOPS
Once the system takes its share, this machine leaves 13.00 GiB for the model: room for 9 of the 26 models we measure at the reference compression, and the heaviest one that fits is gpt-oss-20b. And how fast does it answer? That’s not down to the chip, but to its 83 GB/s of memory bandwidth (a typical figure for this class of machine).
Left for the model
Estimated
Memory bandwidth
Estimated
Models that fit
Measured by Local AI Scope
The memory you can actually use
System RAM is shared with the operating system and every application you have open. The reserve here is deliberately conservative; you can change it in the tool.
| Memory | GiB |
|---|---|
| Memory on the box | 16 |
| Reserved for the system | −3.00 |
| Left for the model | 13.00 |
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.
83 GB/s — Estimated · no single manufacturer: this is a class of machine, not a product. The figure is a conservative estimate for that class, not a specification.
Compute: 1.2 TFLOPS (estimate for this class of machine) — Estimated · no single manufacturer: this is a class of machine, not a product. The figure is a conservative estimate for that class, 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 | 32k |
| 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 | 32k |
| qwen3.5-9b | Q4_K_M |
5.29 GiB | yes | yes | yes | 128k |
| gemma4-12b | Q4_K_M |
6.63 GiB | yes | yes | yes | 128k |
| gpt-oss-20b | Q4_K_M |
10.83 GiB | yes | yes | no | 32k |
| mistral-small-24b | Q4_K_M |
13.35 GiB | no | no | no | — |
| qwen3.8-27b | Q4_K_M |
15.33 GiB | no | no | no | — |
| muse-glimmer-30b | Q4_K_M |
15.61 GiB | no | no | no | — |
| qwen3.6-27b | Q4_K_M |
15.66 GiB | no | no | no | — |
| gemma4-26b-a4b | Q4_K_M |
15.78 GiB | no | no | no | — |
| gemma4-31b | Q4_K_M |
17.07 GiB | no | no | no | — |
| qwen3-coder-30b | Q4_K_M |
17.28 GiB | no | no | no | — |
| 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 — 9 of the 26 models measured
- qwen3-1.7b
Q4_K_M— context 32k tokens - qwen3-4b-2507
Q4_K_M— context 32k 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 32k tokens - qwen3.5-9b
Q4_K_M— context 128k tokens - gemma4-12b
Q4_K_M— context 128k tokens - gpt-oss-20b
Q4_K_M— context 32k tokens
Models that do not fit — 17 of the 26 models measured
- mistral-small-24b
Q4_K_M— short by 1.65 GiB - qwen3.8-27b
Q4_K_M— short by 3.26 GiB - muse-glimmer-30b
Q4_K_M— short by 3.41 GiB - qwen3.6-27b
Q4_K_M— short by 3.59 GiB - gemma4-26b-a4b
Q4_K_M— short by 3.73 GiB - gemma4-31b
Q4_K_M— short by 5.83 GiB - qwen3-coder-30b
Q4_K_M— short by 5.33 GiB - qwen3.6-35b-a3b
Q4_K_M— short by 8.37 GiB - flint-40b
Q4_K_M— short by 11.42 GiB - nemotron-3.5-lightning-30b
Q4_K_M— short by 11.43 GiB - gpt-oss-120b
Q4_K_M— short by 46.28 GiB - llama4-scout-17b
Q4_K_M— short by 49.30 GiB - nemotron-3.5-super-120b
Q4_K_M— short by 53.00 GiB - qwen3.8-flash-next
IQ4_XS— short by 75.02 GiB - deepseek-v4-flash
IQ4_XS— short by 115.29 GiB - glm-5.3-flash
Q4_K_M— short by 174.80 GiB - glm-5.2
Q4_K_M— short by 422.12 GiB
Willing to compress a bit more? Then not everything on this list is out of reach. In the 13.00 GiB of RAM left for the model, mistral-small-24b runs at Q3_K_M (10.69 GiB) with up to 8k tokens of context, and gemma4-26b-a4b runs at Q3_K_M (11.85 GiB) with up to 8k 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 with M4 and 16 GB unified memory — 13.00 GiB left for the model, 9 / 26 models
- Mac mini with M6 and 16 GB — 13.00 GiB left for the model, 9 / 26 models
- 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
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.
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».