Beyond Independent Optimization: Compression, MoE Routing, and Quantization Interactions in Multimodal Edge Intelligence

2026-07-23

AI Summary

This work addresses the limitations of existing edge-based multimodal intelligent systems, which typically optimize components such as compression, routing, and quantization in isolation, thereby overlooking their intricate couplings and failing to balance latency, energy consumption, and accuracy. The study systematically investigates the interaction mechanisms among visual/video token compression, KV caching, Mixture-of-Experts (MoE) routing, and low-bit quantization in large vision-language models, proposing a unified co-design framework. It introduces “temporal routing consistency” as a novel diagnostic metric for video MoE and advocates for routing-aware compression, cross-modal cache management, and hardware-aware design. The analysis reveals critical trade-offs—such as how compression perturbs feature distributions affecting routing decisions, how quantization interferes with expert assignment, and how caching strategies govern the retention of multimodal evidence—thereby establishing foundational design principles and open pathways for efficient edge multimodal systems.

Abstract

Efficient multimodal inference is increasingly constrained not only by model quality or FLOP count, but also by the cost of preserving, moving, routing, caching, and quantizing multimodal representations under latency, memory, and energy constraints. This paper reviews recent advances in efficient vision-language and multimodal large language models, covering visual token compression, video token management, KV-cache optimization, Mixture-of-Experts (MoE) routing, low-bit quantization, edge deployment, and hardware-aware benchmarking. We argue that these techniques cannot be treated as independent optimizations. Visual token compression alters downstream feature distributions and MoE routing decisions, routing behavior affects expert utilization and quantization sensitivity, quantized router logits influence expert assignment, KV-cache policies determine retained multimodal evidence, and hardware constraints often transform computational savings into memory and communication bottlenecks. We organize the literature around these interactions and identify key design trade-offs, including accuracy versus token budget, static versus adaptive compression, sparse routing efficiency versus expert collapse, and low-bit inference versus modality-specific degradation. Finally, we introduce Temporal Routing Consistency as a diagnostic for video MoE models and highlight open research directions in routing-aware compression, cross-modal cache management, hardware-aware co-design, and unified benchmarking for multimodal edge intelligence.

Problem

• multimodal edge intelligence
• compression
• MoE routing
• quantization
• hardware constraints

Innovation

• Mixture-of-Experts routing
• visual token compression
• low-bit quantization
• KV-cache optimization
• Temporal Routing Consistency