Translate text offline with a local neural MT model

NLLB-200-distilled covers 200 languages in a single model; the Opus-MT family gives smaller, faster models for one specific language pair. Both run here on your GPU, which means no character cap, no per-word pricing and no document leaving your machine — the usual reason people cannot paste a contract or a medical note into an online translator. Quality sits below the big commercial engines on rare pairs and is competitive on common ones.

offline translatorfree translation no limitnllb-200 demoopus-mt browserprivate document translationneural machine translation onlinetranslate without apilocal ai translator

Frequently asked questions

Which is better, NLLB-200 or Opus-MT?

Opus-MT is usually better and much smaller for a single well-supported pair such as English↔German. NLLB-200 is the one to use for low-resource languages or when you need many pairs from one download.

Does it work offline?

Yes, once a model is cached. Weights are stored in the Cache Storage API under the key "transformers-cache", so a reload with no network still runs the model. Call "Make storage persistent" first — browsers may evict non-persistent caches under disk pressure.

Models available for this task

NLLB-200 Distilled 600M — Xenova/nllb-200-distilled-600M, 917 MB, 600M params
Marian EN → ZH — Xenova/opus-mt-en-zh, 123 MB, 77M params
Marian EN → ES — Xenova/opus-mt-en-es, 123 MB, 77M params
Marian EN → FR — Xenova/opus-mt-en-fr, 116 MB, 75M params
Marian EN → DE — Xenova/opus-mt-en-de, 115 MB, 74M params
Marian Multi → EN — Xenova/opus-mt-mul-en, 121 MB, 77M params
Marian EN → HI — Xenova/opus-mt-en-hi, 121 MB, 76M params

Explore the rest of the playground

Text & language models

LLM chat — Chat with a local LLM running on your own GPU
Text embeddings — Generate text embeddings locally, in the browser
Summarization — Summarise long text with a model running on your machine
Sentiment analysis — Sentiment analysis that runs in your browser
Zero-shot text classification — Classify text into labels you make up on the spot
Named entity recognition — Named entity recognition, running on your own machine
Question answering — Ask questions about a passage, get the exact answer span
Fill-mask — Watch a masked language model guess the missing word

Guides & comparisons

Model catalog — The model catalog: what actually runs in a browser
WebGPU vs WASM — WebGPU vs WebAssembly for running models in a browser
Browser AI vs cloud API — Browser inference vs a hosted API: an honest comparison
Transformers.js vs WebLLM — Transformers.js vs WebLLM
Whisper model sizes — Choosing a Whisper size for browser transcription
fp32 vs q8 vs q4 — Picking a quantization level for browser inference
Why no image generation — Why this playground has no text-to-image generation
FAQ — Frequently asked questions about browser-based AI
About — About this project

This playground needs JavaScript and WebGPU. Models are downloaded to your browser and executed on your own GPU, so there is nothing to show without a script engine. Enable JavaScript, or read the FAQ for what the site does.