DeepSeek's DSpark boosts throughput 51% to 400% with semi-autoregressive speculative decoding.

DeepSeek released DSpark plus a full open-source training and eval stack called DeepSpec. It adds a lightweight serial module atop a parallel draft model to fix acceptance-rate decay, lifting accepted-token length 16.3% to 30.9% over Eagle3 and DFlash. The paper hit #1 on HN at 510 points. (DeepSeek / GitHub) The enhanced V4 Flash and Pro checkpoints are already live. Inference throughput is the cost wall for anyone serving models at scale, and an 80%-ish speedup with open training code is a real lever, not a benchmark trophy.