How do you implement offline AI for real-time language translation?

Offline translation requires specialized models optimized for bilingual or multilingual translation. Model options: NLLB (No Language Left Behind) by Meta supports 200+ languages in a single model (distilled versions at 600M and 1.3B parameters), MarianMT provides lightweight bilingual models (one per language pair, 300MB each), and Madlad-400 covers 400+ languages. Architecture: encoder-decoder transformer models where the encoder processes source text and the decoder generates target language output. Implementation: use CTranslate2 for optimized inference (2-4x faster than PyTorch, supports INT8 quantization), ONNX Runtime for cross-platform deployment, or llama.cpp for GGUF-converted translation models. Real-time optimization: sentence-level translation for low latency (50-200ms per sentence), sliding window for document translation, vocabulary caching for frequent language pairs, and KV-cache reuse for contextually related sentences. Quality enhancement: implement terminology databases for domain-specific translations (medical, legal, technical), glossary injection at inference time, and post-editing workflow for critical content. Mobile deployment: use MLKit or Core ML for on-device translation, implement language detection for automatic source language identification, and cache frequently translated phrases. Production architecture: microservice wrapping the translation model with language detection preprocessing, sentence segmentation (using pragmaticsegmenter or similar), batch processing for document translation, and quality estimation scoring to flag low-confidence translations for human review.