This paper investigates a critical design decision in the practice of massively multilingual continual pre-training — the inclusion of parallel data. Specifically, we study the impact of bilingual translation data for massively multilingual language adaptation of the Llama 3 family of models to 500 languages. To this end, we construct a bilingual translation corpus named OUR_DATA, containing data from more than 2,500 language pairs. Subsequently, we develop the OUR_MODEL Llama 3 suite of four massively multilingual models — continually pre-trained from the Llama 3 family of base models extensively on diverse data mixes up to 671B tokens — and explore the effect of continual pre-training with or without bilingual translation data. Comprehensive evaluation across 7 tasks and 12 benchmarks demonstrates that bilingual data tends to enhance language transfer and performance, particularly for low-resource languages. We open-source the OUR_DATA corpus, OUR_MODEL Llama 3 suite artefacts, code, and model generations.