Powering Custom Translation Models for Smartphones | Case study | Liquid AI

The challenge

A top smartphone brand needed real-time, on-device translation—but traditional models consumed too much RAM, were too slow for live conversations, and couldn’t adapt efficiently across languages.

Key Obstacles

• Memory constraints: High RAM usage limited multi-language support
• Slow inference: Laggy performance hurt real-time use cases
• Complex customization: No easy way to optimize for specific language pairs

Our solution

We built lightweight, language-specific models using Liquid’s customization stack and Edge SDK, optimizing them for low memory use and high speed on smartphones.

The results

The brand launched fast, accurate translation across devices—even with limited RAM.

• Dramatically reduced memory usage
• Real-time translation speeds for live conversations
• Custom-optimized models for priority languages
• Non-ML teams could deploy models easily