Attention is not all you need · AI Automation Society

Attention is not all you need

Transformers have been the backbone of LLM models for a while… but it’s starting to feel like they’ve hit their natural limits, especially when it comes to long context and efficiency.

Over the last few days, I’ve been diving deep into Mamba, a relatively new architecture based on state space models (SSMs), and honestly, it looks like a very serious contender for “what comes after Transformers.”

What is Mamba?

Mamba is a sequence modeling architecture built on Structured State Space Models.

Instead of comparing every token with every other token (like Transformers' attention mechanism does), it keeps a compact hidden state that gets updated as the sequence progresses.

As a result, you have linear scaling, constant memory, faster inference, and significantly reduced costs.

This is already a significant leap in bringing AI implementation to domains that weren't that well-suited, like edge computing, IOT, etc.

I'll be integrating Mamba into my workflow automation pipelines over the next few weeks, just to see how things work out.

And although transformers aren't going anywhere overnight, I think we're starting to see the cracks. Mamba feels like the kind of shift that makes sense: keep the performance, lose the baggage.

Until next time.

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