Marconi introduces a novel prefix caching system specifically designed for hybrid LLMs, enabling significant performance gains while maintaining accuracy.

• Addresses the unique challenge of caching in hybrid models that combine Attention layers with Recurrent layers
• Achieves up to 12.8x latency reduction and 6.6x throughput improvement
• Introduces a clever state propagation technique for recurrent layers to overcome in-place update limitations
• Maintains full equivalence with non-cached inference to preserve model accuracy

This research is vital for engineering teams deploying hybrid LLMs in production environments, as it substantially reduces computational costs for applications with redundant prefixes like chatbots and code completion.

Marconi: Prefix Caching for the Era of Hybrid LLMs