On 27 June 2026, researchers from Peking University and DeepSeek jointly introduced and open-sourced DSpark, an innovative speculative decoding framework designed to optimise Large Language Model (LLM) inference efficiency. Released under an MIT license via the DeepSpec GitHub repository, DSpark functions as an engineering layer rather than a newly trained standalone model. It builds on existing checkpoints by introducing an attached speculative draft module. This framework introduces two core architectural breakthroughs: Semi-Autoregressive Generation: It pairs an enhanced parallel backbone network with lightweight sequential modules (Markov heads) to preserve token dependencies within blocks. This successfully resolves the traditional “acceptance rate decay” over long text sequences, allowing a two-layer DSpark module to outperform traditional five-layer parallel architectures.

Full report : DeepSeek details DSpark, a speculative decoding framework for its V4 models, saying it speeds up AI inference by up to 85% and was tested on Gemma and Qwen.