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We introduce Tiny Aya, a family of open-weight multilingual language models that achieve strong, balanced performance across 70 languages with 3.35 billion parameters through deliberate data curation and training design. The release includes a pretrained base model, a globally balanced instruction-tuned variant, and three region-specialized models targeting Africa, South Asia, Europe, Asia-Pacific, and West Asia. We contribute: (1) a script-aware tokenizer weighting scheme that achieves competitive or superior compression across most writing systems, particularly for underrepresented scripts; (2) a posttraining pipeline combining FusioN, a multi-teacher synthetic data generation framework, with region-aware supervised finetuning and SimMerge, a predictive model merging method that preserves global consistency while retaining regional gains; (3) a comprehensive multilingual evaluation suite spanning translation, understanding, reasoning, generation, safety, and cultural awareness across up to 66 languages. Despite its compact size, Tiny Aya outperforms Gemma3-4B on translation in 46 of 55 languages, is competitive with or outperforms similar-sized models on generative benchmarks with substantially lower cross-language variance, and achieves the highest mean safe response rate (91.1%) on multilingual safety benchmarks. Quantized variants run on smartphones and quantization degradation attenuates for lower-resource languages . We release all models to support research into efficient, inclusive multilingual AI.