DeepSeek's DSpark Grafts Speculative Decoding onto Target Models for Faster LLM Inference

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DeepSeek has released a research paper introducing DSpark, a new approach to speculative decoding that attaches draft heads directly onto the target language model rather than training a separate smaller model. The technique reuses the target model's own intermediate representations, reducing layer duplication and architectural overhead associated with traditional speculative decoding setups. DSpark is designed to work alongside Multi-Token Prediction rather than replace it, and the speculative tokens it generates are still validated against the main model in a single forward pass, ensuring output quality remains identical to the original model. In DeepSeek's experiments, the method was tested on top of Step and Qwen 3.6 models, and the paper notes particular efficiency gains on modern hardware such as NVIDIA H100s and DGX Spark. The code and paper have been published openly in the deepseek-ai/DeepSpec GitHub repository, making it immediately accessible to developers working on LLM inference optimization.

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