While the two models share the same design philosophy , they differ in scale and attention mechanism. Sarvam 30B uses Grouped Query Attention (GQA) to reduce KV-cache memory while maintaining strong performance. Sarvam 105B extends the architecture with greater depth and Multi-head Latent Attention (MLA), a compressed attention formulation that further reduces memory requirements for long-context inference.
（一）扰乱机关、团体、企业、事业单位秩序，致使工作、生产、营业、医疗、教学、科研不能正常进行，尚未造成严重损失的；，推荐阅读新收录的资料获取更多信息
Более 100 домов повреждены в российском городе-герое из-за атаки ВСУ22:53，推荐阅读新收录的资料获取更多信息
20:10, 2 марта 2026Мир
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