DeepSeek DSpark: Speculative Decoding Accelerates LLM Inference 50–400%

Short summary

DeepSeek's DSpark uses semi-autoregressive speculative decoding to accelerate LLM inference 50–400% without retraining by having a small draft model propose token blocks while a large model verifies them in parallel. Overcomes limitations of prior autoregressive and parallel methods through improved block acceptance and confidence-scheduled verification. Production-ready with open-source DeepSpecs repo and validated on V4 models, Qwen, and Gemma.

• •Speculative decoding speeds inference 50–400% by proposing and verifying token blocks in parallel
• •Semi-autoregressive draft head improves acceptance; confidence-scheduling reduces verification waste
• •Tested on M2 Max; production-deployed on DeepSeek V4; open-source implementation available

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