Transformer models have been successful in various AI applications but struggle with long texts due to memory usage and processing speed limitations. This issue affects real-world applications like report analysis, contract review, and chat transcripts. Jamba, developed by AI21 Labs, offers a solution by using a sequential approach inspired by human comprehension and combining Transformer layers with Mamba layers and Mixture-of-Experts modules. Jamba's hybrid architecture allows for high throughput and reduced memory footprint when processing long contexts, making it more efficient and cost-effective than traditional dense models.