Attention was never enough: Tracing the rise of hybrid LLMs

Transformer-based large language models face computational challenges due to the quadratic complexity of their self-attention mechanism, which becomes expensive with long contexts. A new class of hybrid LLMs has emerged, combining Transformer architecture with Mamba, a selective state-space model designed for greater efficiency and throughput. This architectural shift began with the original Mamba paper and was followed by models like AI21's Jamba, NVIDIA's MambaVision, and Mistral's Codestral Mamba. These hybrid models demonstrate significant improvements in handling long contexts, reducing latency, and scaling efficiently without sacrificing performance, marking a significant evolution in foundation model design.