DeepSeek-V4搞了个新框架DSpark，把生成和验证分开调度，用户实际体验加速60%到85%，还不会拖慢系统吞吐。
DSpark是一种新型投机解码框架，通过半自回归架构（并行骨干网络+轻量级顺序模块）保持草稿质量，缓解后缀衰减。它采用置信度调度验证，根据前缀存活概率和引擎吞吐量动态调整每轮验证长度。在离线多领域基准测试中，DSpark的接受长度显著优于现有自回归和平行草稿器。在DeepSeek-V4服务系统的真实用户流量下，相比生产基线MTP-1，DSpark在维持吞吐量不变时单用户生成速度提升60%至85%。DSpark还通过防止高并发下吞吐量严重退化，使服务系统达到了此前无法实现的性能层级。
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token dependencies. Furthermore, indiscriminately verifying these extended blocks wastes critical batch capacity on tokens with high rejection risks, severely degrading throughput in high-concurrency serving systems. We introduce DSpark, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification. To maintain draft quality, DSpark utilizes a semi-autoregressive architecture, coupling a parallel backbone with a lightweight sequential module, to introduce intra-block dependency modeling and mitigate suffix decay. To optimize system efficiency, DSpark employs confidence-scheduled verification, dynamically tailoring the verification length for each request based on estimated prefix survival probabilities and engine-specific throughput profiles. On offline benchmarks across diverse domains, DSpark substantially improves the accepted length over state-of-the-art autoregressive and parallel drafters. When deployed within the DeepSeek-V4 serving system under live user traffic, DSpark successfully mitigates verification waste. Compared to the established production baseline (MTP-1), DSpark accelerates per-user generation speeds by 60 to 85 percent at matched throughput levels. More importantly, by preventing severe throughput degradation under strict interactivity constraints, it enables performance tiers that were previously unattainable, shifting the Pareto frontier of our serving system.