Mamba3 in Three Animations
📰 Medium · LLM
Learn how Mamba3 improves upon Mamba2 through three key animations, understanding the changes in state-space models and their implications
Action Steps
- Watch the three animations to visualize the changes from Mamba2 to Mamba3
- Understand the state-space model equation: h_t = a · h_{t-1} + b_t · x_t
- Apply the concept of linear recurrence in sequence processing
- Analyze the role of model activations in driving the animations
- Explore the concept demo of the rank idea in the MIMO animation
Who Needs to Know This
Machine learning engineers and researchers can benefit from understanding the advancements in Mamba3, particularly those working with state-space models and recurrent neural networks
Key Insight
💡 Mamba3 builds upon Mamba2 by introducing changes that enhance the state-space model, including improvements in linear recurrence and model activations
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💡 Mamba3 explained in 3 animations! Learn about the key changes and improvements in state-space models
Key Takeaways
Learn how Mamba3 improves upon Mamba2 through three key animations, understanding the changes in state-space models and their implications
Full Article
Title: Mamba3 in Three Animations
URL Source: https://pub.towardsai.net/mamba3-in-three-animations-f1573608b169?source=rss------llm-5
Published Time: 2026-07-01T00:15:19Z
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# Mamba3 in Three Animations
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[Jun Nishimura](https://pub.towardsai.net/@j.tech.reveil?source=post_page---byline--f1573608b169---------------------------------------)
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_The three changes that turn Mamba-2 into Mamba-3 — each one watched, not just described. Two of the three animations are driven by real model activations; the third (MIMO) is a concept demo of the rank idea — all grounded in the real architecture._
State-space models (SSMs) like Mamba run a tiny linear recurrence down the sequence:
h_t = a · h_{t-1} + b_t · x_t # update the state
y_t = c_t · h_t # read it back out
That’s the whole engine. A hidden state `h` decays a little each step (`a`), absorbs the current token (`b_t·x_t`), and gets read out through `c_t`. It's an RNN you can also run as one big parallel scan — linear-time, constant memory at inference, no quadratic at
URL Source: https://pub.towardsai.net/mamba3-in-three-animations-f1573608b169?source=rss------llm-5
Published Time: 2026-07-01T00:15:19Z
Markdown Content:
[Sitemap](https://pub.towardsai.net/sitemap/sitemap.xml)
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# Mamba3 in Three Animations
[](https://pub.towardsai.net/@j.tech.reveil?source=post_page---byline--f1573608b169---------------------------------------)
[Jun Nishimura](https://pub.towardsai.net/@j.tech.reveil?source=post_page---byline--f1573608b169---------------------------------------)
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5 min read
·
1 hour ago
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_The three changes that turn Mamba-2 into Mamba-3 — each one watched, not just described. Two of the three animations are driven by real model activations; the third (MIMO) is a concept demo of the rank idea — all grounded in the real architecture._
State-space models (SSMs) like Mamba run a tiny linear recurrence down the sequence:
h_t = a · h_{t-1} + b_t · x_t # update the state
y_t = c_t · h_t # read it back out
That’s the whole engine. A hidden state `h` decays a little each step (`a`), absorbs the current token (`b_t·x_t`), and gets read out through `c_t`. It's an RNN you can also run as one big parallel scan — linear-time, constant memory at inference, no quadratic at
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