Mamba-3 is a non-Transformer language-model architecture that compresses prior context into a fixed-size state. According to the briefing, the time for each decoding step does not grow with sequence length.
The text suggests comparing this state-space approach with Transformer KV caching, particularly when evaluating long-context inference. It gives no quantitative results, so it does not support a conclusion that one approach is faster in every scenario.
For a useful comparison, test the alternatives with the same models, hardware, inputs, and parameters. Record per-step latency, context length, and memory use, and repeat measurements to identify variation.
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