Hybrid Architectures: Marrying Transformers with Mamba (SSMs)
Introduction: The Quadratic Bottleneck Transformers revolutionized AI, but they have a fundamental flaw: quadratic scaling. Processing a sequence of length n requires O(n²) operations due to self-attention. Every token attends to every other token, creating an all-to-all comparison: Context length: 1K 10K 100K 1M Operations: 1M 100M 10B 1T Time (relative): 1× 100× 10,000× 1,000,000× This makes long-context processing prohibitively expensive. Enter State Space Models (SSMs), specifically Mamba: a new architecture that processes sequences in linear time O(n) while maintaining long-range dependencies. ...