Inference
Mamba
Mamba is a sequence model architecture built on selective state space layers, where the SSM parameters depend on the current token so the model can choose what to remember or forget.
In depth
Mamba is a sequence model architecture built on selective state space layers, where the SSM parameters depend on the current token so the model can choose what to remember or forget. It drops attention entirely, scales linearly with sequence length, and the paper reports about 5x higher inference throughput than transformers. Mamba layers are now most often used inside hybrid models such as NVIDIA's Nemotron family rather than on their own.
Primary source: arxiv.org
Example
Mamba layers are now most often used inside hybrid models such as NVIDIA's Nemotron family rather than on their own.
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FAQ
Common questions
What is Mamba?
Mamba is a sequence model architecture built on selective state space layers, where the SSM parameters depend on the current token so the model can choose what to remember or forget.
Why does Mamba matter for AI developers?
Mamba sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Mamba?
Developers Digest publishes tutorials and videos that cover Inference topics including Mamba. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
Inference
State Space Models (SSM)
State space models are sequence models that carry a fixed-size hidden state forward through the input, updating it with learned linear equations, rather than comparing every token to every other token like attention.
Inference
Mixture of Experts (MoE)
A model architecture that routes each input to a small subset of specialized sub-networks ("experts") rather than activating the entire model.
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