Choosing a model on a low-RAM machine¶

The model is loaded once and stays resident for as long as the daemon runs, so on a small machine the question is not "how fast is it" but "does it fit alongside everything else".

Measured here

13^th Gen Intel Core i7-1370P · Ubuntu 24.04 · Python 3.14 · faster-whisper, int8 on CPU. "Peak RSS" is the whole Python process at its high-water mark during a decode — interpreter, libraries and model together, which is what your system actually has to find.

The numbers¶

Model	On disk	Peak RSS	Decode, 11 s clip	WER¹
`tiny.en`	78 MB	285 MB	0.58 s	4.82 %
`base.en` (default)	148 MB	370 MB	0.79 s	4.07 %
`small.en`	486 MB	712 MB	1.93 s	2.59 %

¹ LibriSpeech test-clean, 200 utterances — the method is on the benchmarks page. Real dictation is harder than that benchmark; treat WER as a comparison between models, not a promise.

Resident memory is roughly twice the file on disk. That surprises people who size a machine from the download.

What to pick¶

You have	Use	Why
≥ 8 GB	`base.en` (default)	370 MB is noise on that machine
4 GB	`base.en`, or `tiny.en` if it swaps	it fits, but check against your browser
2 GB	`tiny.en`	285 MB is the smallest resident footprint available
< 2 GB	`tiny.en`, and expect trouble	nothing here is tuned for it

[stt]
model = "tiny.en"

Then yazses restart. Confirm what is actually loaded:

yazses status     # shows the model, and p50/p95 decode latency once you have used it

Is it swapping?¶

The failure mode on a small machine is not an error — it is dictation that used to take a second and now takes fifteen, because the model is being paged back in between utterances.

# While the daemon is running:
ps -o rss=,comm= -C python3 | sort -rn | head -3     # resident KB
free -m                                              # is `swap used` climbing?

If swap used grows while you dictate, drop a model size. No amount of tuning recovers from swapping a model in and out.

What does not reduce memory¶

[stt] compute_type is already int8, which is the smallest option that ships. float16/float32 make it larger, not smaller.
[stt] cpu_threads changes CPU use, not memory — see CPU and battery. Measured: 363–373 MB across every thread setting, i.e. no effect.
Turning off features helps only if they load their own model. The optional ones that do are LLM cleanup, gaze and Cocktail Filter; the rest are pure logic.

The multilingual models¶

tiny/base/small (without .en) understand many languages and are roughly the same size as their English-only counterparts, but are less accurate on English. Only use one if you dictate a non-English language — see [stt] language.

What is not measured here¶

A machine that actually has 2 GB. These footprints were measured on a large machine; the "you have" table above is arithmetic on those numbers, not a test on small hardware. If you run YazSes on a Raspberry Pi or an old netbook, tell us what happened — that report is worth more than this table.
large-v3. It is not in the table because it is not a sensible choice on a low-RAM machine, which is what this page is about.