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Mixture-of-recursions delivers 2x faster inference—Here’s how to implement it
Mixture-of-Recursions (MoR) is a new AI architecture that promises to cut LLM inference costs and memory use without sacrificing performance.
The architecture, called Mixture-of-Recursions(MoR), significantly improves model accuracy and delivers higher throughput compared with vanilla transformers, even when constrained by the same parameter count and compute budget. It decides how many times a shared block of layers should be applied based on a token’s complexity, or its required “depth of thinking.” This directs computation only where it is most needed, avoiding wasted cycles on easy-to-process parts of the input. By dynamically adjusting the processing depth for each segment of a video or audio stream, MoR could unlock even greater cost savings and performance improvements, bringing the power of large-scale AI to a wider range of enterprise applications.
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