A Comparative Survey of Inference Acceleration for DLLMs against AR-LLMs: No Free Lunch

Autoregressive large language models (AR-LLMs) have achieved remarkable success, but their inherently sequential decoding process remains a fundamental bottleneck for efficient inference. Diffusion large language models (DLLMs), with bidirectional modeling and parallel token generation, offer a promising alternative to break this token-by-token limitation. Yet despite rapid progress, the practical inference efficiency of current DLLMs remains unclear. From a verification perspective, this survey establishes a systematic taxonomy of existing acceleration methods, benchmarks representative techniques under a unified experimental setting, and further evaluates strong strategy combinations to quantify the gap between mainstream DLLM inference methods and state-of-the-art AR baselines. Specially, the overall analysis highlights that the parallel decoding efficiency of DLLMs still remains a significant lag compared to the decoding efficiency of AR-LLMs under inference acceleration. We provide an in-depth experimental analysis about the underlying trade-offs among generation quality, latency, and system compatibility, and build up a standard evaluation bench open to the community. Remaining bottlenecks are also summarized, together with future directions for more practical and competitive DLLM inference. Code is available at \url{https://github.com/haoyun-jiang/DLLM-AccelEval}.