Budgeted LoRA: Distillation as Structured Compute Allocation for Efficient Inference

Researchers introduce Budgeted LoRA, a distillation framework that compresses large language models by treating model compression as a structured compute allocation problem. The method achieves up to 4.05x speedup in inference through selective dense component removal and adaptive low-rank allocation, controlled by a single compute budget parameter.

Budgeted LoRA addresses a critical gap in current model optimization techniques. While existing parameter-efficient methods like standard LoRA reduce training costs, they fail to improve inference speed because the dense backbone remains unchanged. This new framework reframes the problem by introducing a global compute budget that determines the target fraction of dense computation, allowing the model to intelligently redistribute capacity across dense and low-rank pathways through module-level retention coefficients and adaptive allocation.

The research builds on growing recognition that inference efficiency matters as much as training efficiency in production environments. As organizations deploy large language models at scale, inference costs dominate total operational expenses. Prior work focused on reducing parameters without fundamentally restructuring computation pathways, leaving performance gains on the table. Budgeted LoRA's approach of treating compression as compute allocation represents a shift toward holistic model optimization.

The empirical results demonstrate practical value: moderate budgets match standard LoRA perplexity with 1.74x speedup, while aggressive budgets achieve 4.05x speedup with acceptable accuracy tradeoffs. Notably, the method preserves performance on in-context learning tasks better than approaches focused solely on perplexity reduction, suggesting structural efficiency matters more than parameter count alone.

For organizations deploying language models, this framework enables fine-grained control over the inference-quality tradeoff through a single parameter, facilitating easier optimization across heterogeneous hardware constraints. The preservation of in-context learning capabilities is particularly significant for applications requiring reasoning or dynamic adaptation.

• →Budgeted LoRA achieves 4.05x inference speedup with moderate perplexity degradation using structured compute allocation
• →The framework treats model compression as a budget problem rather than parameter removal, enabling intelligent dense-to-low-rank redistribution
• →Method preserves in-context learning performance better than perplexity-focused compression approaches
• →Single compute budget parameter enables flexible control of inference-quality tradeoffs without architecture changes
• →Results suggest inference efficiency depends more on computation distribution than total parameter count