eagle-3-f992e7c8·2 events·first seen Aliases: EAGLE-3
Researchers from Peking University and DeepSeek introduced DSpark, a speculative decoding module that dynamically adjusts verification depth based on server load, achieving 57–85% faster per-user token generation and 51–52% higher total throughput compared to DeepSeek's previous production drafter. The team released checkpoints DeepSeek-V4-Pro-DSpark and DeepSeek-V4-Flash-DSpark on Hugging Face under an MIT license, with the draft module attaching to frozen target model weights. Key innovations include a parallel drafting backbone (adapted from DFlash), a Markov head for sequential token coherence correction, a calibrated confidence head, and a load-aware scheduler that trades draft length against server capacity. Results generalize across model families including Qwen3 and Gemma4.
Graft is a training-free framework that improves speculative decoding by coupling dynamic-depth pruning with retrieval-based token compensation. Pruning reduces VRAM and compute overhead while freeing budget for retrieval, which fills topological gaps in the draft tree with near-zero additional cost. On short-context benchmarks, Graft achieves up to 5.41× speedup and improves average speedup over EAGLE-3 by up to 21.8% on Qwen3-235B. The method is evaluated across short- and long-context settings and extended to block-drafting paradigms.