GLM-5.2 + DSpark Is INSANE (Beats Claude + 85% Faster + OpenSource)

Speculative decoding is a technique that accelerates large language model inference by having a smaller draft model generate multiple tokens in parallel, then using the main model to verify these guesses in a single pass, achieving up to 85% speedup while maintaining identical output quality. This approach addresses the fundamental bottleneck of autoregressive token generation, where traditional LLMs must process one token at a time through the entire network. The technique works by proposing several tokens ahead and checking them simultaneously, keeping correct guesses and only verifying uncertain predictions, which dramatically reduces GPU trips without any quality loss.