What You’ll Build
In about 90 minutes, you will have a translation automation system that processes documents, website content, product strings, and marketing copy across 30+ language pairs with context-aware quality that maintains brand voice, technical accuracy, and cultural nuance. The system handles 100,000+ words per hour on a single dedicated GPU server with translation memory and glossary enforcement built in.
Professional translation costs $0.10-0.30 per word and takes days. Generic machine translation is fast but misses context, produces inconsistent terminology, and requires extensive post-editing. LLM-powered translation on open-source models bridges the gap: it understands context across paragraphs, follows glossaries, maintains consistent terminology, and adapts tone for different content types, all at machine speed with zero per-word costs.
Architecture Overview
The workflow has four components: a content ingestion layer that extracts translatable strings from various file formats preserving structure, a translation engine powered by a multilingual LLM via vLLM, a RAG-backed translation memory and glossary system, and a quality assurance module that checks consistency and completeness. LangChain orchestrates the multi-step translation pipeline with format preservation.
The translation memory stores previously approved translations as embeddings. When new content arrives, the system retrieves similar previously translated segments to ensure consistency. The glossary enforcer verifies that specific terms (product names, technical vocabulary, legal terms) use the approved translation in each target language. This architecture produces translations that improve over time as the translation memory grows.
GPU Requirements
| Translation Volume | Recommended GPU | VRAM | Words Per Hour |
|---|---|---|---|
| Up to 50K words/day | RTX 5090 | 24 GB | ~50,000/hr |
| 50K – 500K words/day | RTX 6000 Pro | 40 GB | ~120,000/hr |
| 500K+ words/day | RTX 6000 Pro 96 GB | 80 GB | ~250,000/hr |
Translation quality scales strongly with model size. A 70B multilingual model produces significantly better output for complex language pairs and nuanced content compared to an 8B model. For high-resource language pairs like English-German, even smaller models perform well. See our self-hosted LLM guide for multilingual model recommendations.
Step-by-Step Build
Deploy vLLM with a multilingual model on your GPU server. Set up the translation memory database and populate the glossary with your approved terminology in each target language. Build the content ingestion pipeline supporting XLIFF, JSON, PO, Markdown, HTML, and DOCX formats.
# Context-aware translation prompt
TRANSLATE_PROMPT = """Translate the following content from {source_lang} to {target_lang}.
Content type: {content_type}
Brand voice: {brand_guidelines}
Glossary terms to enforce: {glossary_entries}
Translation memory matches: {tm_matches}
Source text:
{source_text}
Instructions:
- Maintain the same tone and register as the source
- Use glossary terms exactly as specified
- Preserve all formatting markers and variables ({variable_name})
- Adapt cultural references appropriately
- If TM matches exist, maintain consistency with previous translations
Translation:"""The QA module runs automated checks: glossary compliance verification, placeholder preservation, length ratio validation (catching truncations or over-expansions), and consistency checks against the translation memory. Build a reviewer interface where linguists approve or correct translations, with corrections automatically updating the translation memory. Follow the vLLM production guide for throughput optimisation and add a query interface for translators to check terminology decisions.
Performance and Quality Metrics
On an RTX 6000 Pro running Llama 3 70B in 4-bit quantisation, translation throughput reaches 120,000 words per hour across major language pairs. BLEU scores against professional human translations average 42-48 for technical documentation and 38-44 for marketing copy, placing LLM translation quality between raw machine translation and professional post-edited output. Glossary compliance reaches 97% with enforced term checking.
The translation memory reduces processing time for repeated or similar content by 40-60%, as cached translations bypass GPU inference entirely. For software localisation workflows, the system integrates with CI/CD pipelines to automatically translate new strings when code merges, keeping all language versions current through AI-powered automation.
Deploy Your Translation Workflow
Automated translation with translation memory and glossary enforcement delivers consistent, high-quality multilingual content at machine speed. No per-word fees, no content sent to external translation APIs, and quality that improves with every correction. Launch on GigaGPU dedicated GPU hosting and automate your localisation pipeline. Browse more automation patterns in our use case library.
