Decentralized Speculative Decoding (DSD) has emerged as a breakthrough framework for enhancing large language model (LLM) inference on distributed networks. Integrated into Parallax, DSD addresses the challenge of communication latency between nodes, which traditionally slows down token generation. By transforming latency into additional compute bandwidth, DSD achieves a 2.6× increase in throughput and a 37% reduction in communication, all without compromising accuracy. DSD introduces two key innovations: Batch Settlements Decoding and Adaptive Verification. Batch Settlements Decoding reduces synchronization bottlenecks by bundling multiple tokens into a single verification cycle, while Adaptive Verification optimizes token validation based on their importance, enhancing speed by 15-20% without quality loss. These advancements allow for higher throughput and reduced dependency on WAN latency, making DSD a powerful tool for tasks like agent reasoning and code generation across remote clusters.