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Transformer-based models experience significant performance issues when handling long texts, as their memory usage and processing speed suffer due to architectural scaling limitations. The attention mechanism scales quadratically with sequence length, causing a large memory footprint and slow inference, which makes many long-context applications impractical. AI21 Labs' Jamba model is presented as a solution, utilizing a hybrid architecture that combines Transformer layers, Mamba (SSM) layers, and Mixture-of-Experts (MoE) modules. This design allows Jamba to process information sequentially, avoiding the quadratic scaling issues and resulting in higher throughput and a smaller memory footprint for long-context tasks.