back_office_ops · saas · workflow
Grammarly builds a compact on-device spelling and grammar correction model (~1B parameters)
Grammarly's writing assistance goes offline when internet is unavailable because its corrections rely on multiple large models that cannot run on a user's device due to limited memory and processing capabilities.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User writes in real time
Users expect Grammarly to provide real-time suggestions as they write.
Tools used
LlamaT5MLXGQA
Outcome
A compact ~1B parameter model achieves ~210 tokens/second on an M2 Mac with a 70% reduced memory footprint, enabling real-time on-device spelling and grammar corrections without loss in quality.
What failed first
T5, an encoder-decoder model evaluated as a base model candidate, failed the tokenization requirement by converting nonstandard spaces to regular spaces, making it unsuitable.
Results
Time saved50 tokens per second
Volume~210 tokens/second
Grounding & classification
Source type: technical build writeup
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