Online social media platforms have become central to communication and information exchange, however, they also serve as fertile ground for hate speech, offensive language, and bullying targeting individuals and communities.Such content undermines online safety and inclusion, underscoring the need for reliable detection systems-especially in lowresource languages with limited moderation tools.For Bangla, existing work provides valuable resources and models, however, they are mostly single-task (e.g., binary hate/offense) with narrow coverage of key dimensions such as type, severity, and target.We address these gaps by introducing the first multi-task Bangla hate-speech dataset, BanglaMultiHate, one of the largest manually annotated dataset to date.Using this resource, we performed a comparative study across different baselines, monolingual pretrained models, and LLMs under zeroshot, few-shot, and LoRA fine-tuning settings.Our findings show that while LoRA-tuned LLMs rival BanglaBERT, culturally grounded pretraining remains crucial for robust performance.Overall, BanglaMultiHate establishes a stronger benchmark for hate speech detection in low-resource contexts.All data 1 and scripts 2 are released for reproducibility.