Mamba: Can it replace Transformers?

A lot of research effort has gone into making Transformers efficient. Transformers are great, no doubt about that, but they are very resource and data-intensive. Research like Flash Attention, RetNet, and many others show great potential, but somehow Transformer remains the king. In this paper review, we will talk about a completely new architecture called Mamba.

It enjoys fast inference (5× higher throughput than Transformers) and linear scaling in sequence length, and its performance improves on real data up to million-length sequences. Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics as a general sequence model backbone. On language modeling, our Mamba-3B model outperforms Transformers of the same size and matches Transformers twice its size in pretraining and downstream evaluation.

Table of Contents

• Understanding Attention memory requirements
• Other methods to solve memory problems
• Why does Mamba look promising?
• Problems with RNN
• What is a “Structured State Space Model” (SSM)?
• Mamba 🐍
• Hardware Acceleration
• A Simplified SSM Architecture

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