RaNA introduces a novel approach to accelerate large language models by dynamically allocating computational resources based on input complexity.

• Achieves 2-3x speedups with minimal accuracy loss in modern transformers
• Overcomes limitations of neuron-adaptive techniques through rank-adaptive computation
• Works with both MLP and attention layers, unlike previous approaches
• Eliminates costly neuron masking through efficient matrix multiplication

This engineering advancement makes LLMs more practical for resource-constrained environments, reducing inference costs while maintaining performance.

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters