EVAR: Edge Visual Autoregressive Models via Principled Pruning

Next-scale visual autoregressive (VAR) models offer stronggenerative fidelity but remain computationally prohibitive for resource-constrained edge devices. We introduce EVAR, a structured-pruningframework tailored to next-scale VAR models. We find that standardpruning paradigms are poorly matched to multi-scale VAR architec-tures: rapid token growth at later scales dominates Hessian accumu-lation, marginalizing critical coarse scales and amplifying autoregres-sive error cascading. To address this issue, EVAR introduces Pyramid-Aware Optimal Brain Surgeon. Through pyramid-aware Hessian accu-mulation, Pyramid-Aware OBS rebalances scale-wise activation statisticsand biases the solver toward preserving coarse-scale representations whilemaintaining an invertible Hessian estimate for closed-form weight com-pensation. Additionally, we propose Progressive Scale-Aware Distillation(PSAD) to counteract scale-wise gradient imbalance during fine-tuning.On ImageNet benchmarks, EVAR substantially reduces parameters andmodel footprint while retaining competitive quality. On an iOS deploy-ment, EVAR further cuts single-image latency from 494 ms to 277 ms(1.8× speedup), with FID changing only marginally.