Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees

Researchers propose an expert-wise mixed-precision quantization strategy for Mixture-of-Experts models that assigns bit-widths based on router gradient changes and neuron variance. The method achieves higher accuracy than existing approaches while reducing inference memory overhead on large-scale models like Switch Transformer and Mixtral with minimal computational overhead.

This research addresses a critical bottleneck in deploying large sparse language models: memory efficiency during inference. Mixture-of-Experts architectures enable parameter scaling by selectively activating expert subsets, but the sheer parameter count still creates substantial memory requirements. While quantization reduces model size, uniform bit-width quantization degrades accuracy at low precisions, making mixed-precision approaches necessary.

The proposed method's innovation lies in its theoretical grounding. By measuring router gradient changes during training, researchers identify which experts are sensitive to quantization—counterintuitively, experts with smaller gradient changes capture less frequent but critical features requiring higher precision. Combined with analysis of intra-neuron variance to avoid high-noise quantization, this creates an expert-specific precision allocation strategy that outperforms existing heuristics.

For the broader AI infrastructure landscape, this work has meaningful implications. MoE models represent the future of efficient scaling for large language models, with industry leaders adopting variants like Mixtral. Reducing inference memory while maintaining accuracy directly impacts deployment feasibility on edge devices and reduces computational costs for cloud inference providers. This translates to lower latency, reduced energy consumption, and improved accessibility for resource-constrained environments.

The negligible overhead for bit-width assignment is particularly noteworthy—previous mixed-precision methods required substantial computation for allocation, making them impractical. This advancement accelerates the adoption path for efficient MoE deployment in production systems, especially relevant as models scale toward trillion-parameter regimes where memory becomes the primary constraint rather than computation.

• →Expert-wise mixed-precision quantization assigns bit-widths based on router gradient changes and neuron variance, not uniform strategies.
• →Experts with smaller gradient changes are more sensitive to quantization and require higher precision despite lower activation frequency.
• →Method achieves superior accuracy on Switch Transformer and Mixtral compared to existing quantization approaches.
• →Negligible computational overhead for bit-width assignment makes the approach practical for large-scale deployment.
• →Reduces inference memory overhead critical for deploying billion and trillion-parameter MoE models efficiently.