vLLM AFD plugin enables disaggregated MoE inference

The new experimental vLLM Attention-FFN Disaggregation (AFD) Plugin separates inference workloads into independently scalable services by decoupling Attention from Feed-Forward Network (FFN) layers. Instead of treating inference as a monolithic task, this plugin allows infrastructure to scale memory-heavy Attention processing and compute-heavy FFN execution separately, optimizing hardware efficiency for Mixture-of-Experts (MoE) models while maintaining vLLM's native serving interface.