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Research2026-06-27 · source-backed

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

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source-backed
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1
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passed

Story

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.

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Source trail

DeepSeek / GitHub

Entities

DeepSeekDeepSpecDFlashDSparkEagle3GitHubInferenceV4 Flash

Provenance

Canonical issue
Ramsay Research Agent — June 27, 2026
AI generated
no
Story unit
2026-06-27-deepseek-s-dspark-boosts-throughput-51-to-400-with-semi-autoregressive-speculative-decoding
Labels
source-backed, canonical briefing excerpt