Best Text to Text Translation Model? [D]

I'm working on a project that translates any language into English.

So far, I've tried NMT models like NLLB, MADLAD, and SeamlessM4T v2.

The main issue is that they struggle with proper nouns such as:

- names

- places

- dates

- organizations

I also tried LLMs like Gemma 4, Qwen 3 4B, and Aya Tiny Global, but the issue still persists. The LLMs sometimes partially translate or modify entity names as well.

I even tried NER masking / placeholder replacement before translation, but multilingual NER itself becomes a bottleneck. Most NER models only work reliably for a limited set of languages, while my dataset contains 100+ languages, including many low-resource ones.

How do production systems usually handle this problem? Are there better multilingual translation models, multilingual NER approaches, or decoding techniques for preserving entities properly?

Requirements:

- Support for 100+ languages

- Runs locally on an RTX GPU

- Model size under 7B

- English is always the target language.