Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference
A training-free, input-adaptive inference method called Reduced Matrix Multiplication (RMM) reduces Transformer matrix products by selecting informative slices along contraction dimensions without modifying model weights. It provides a smooth accuracy-efficiency trade-off under a retention-ratio control. Evaluated on language models from 1B to 70B parameters, reduction tolerance depends on model family, task, component, and retention ratio, often improving with model scale. RMM remains robust under moderate reduction across discriminative, autoregressive generation, and long-context settings, and extends to multimodal vision-language inference. Mechanistic ablations show attention-side computations are substantially more reducible than MLP components. Wall-clock benchmarks with custom kernels on an NVIDIA A100 show computational savings.
Researchers propose Reduced Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting informative slices along contraction dimensions without modifying model weights. Under a retention-ratio control, RMM provides a smooth and predictable accuracy-efficiency trade-off. Across language models from 1B to 70B parameters, reduction tolerance depends on model family, task, component, and retention ratio, often improving with model scale. Under moderate reduction, RMM remains robust across discriminative, autoregressive generation, and long-context settings, and extends to multimodal vision-language inference. Mechanistic ablations reveal attention-side computations are substantially more reducible than MLP components. Wall-clock benchmarks with custom kernels on an NVIDIA A100 show computational savings.
RMM exploits structural asymmetry in Transformers: attention-side computations are more reducible than MLP components. The method is training-free and input-adaptive, using a retention-ratio control to balance accuracy and efficiency. Custom kernels on NVIDIA A100 demonstrate wall-clock speedups, indicating practical implementation feasibility.
This approach could reduce inference costs for large language models without retraining, making it attractive for deployment. The finding that reducibility often improves with model scale suggests larger models may benefit more, potentially shifting efficiency strategies in model serving.
RMM offers a potential reduction in inference compute costs for LLM providers, enabling more efficient serving and possibly lower pricing. It could extend the practical deployment of large models on existing hardware, improving margins for AI service providers.
Next signals include open-source implementation or integration into inference frameworks, further validation on additional model families and tasks, and exploration of hardware-specific optimizations. If the method proves robust, it may influence model serving architectures and cost models.