GitHub radar
DeepSeek releases its toolbox of speculative decodings
DeepSeek AI has open-sourced their implementation of speculative decoding, a method for significantly speeding up LLM inference without modifying the underlying model.
Speculative decoding is a method for accelerating LLM inference by using a small ‘draft’ model to generate a batch of tokens quickly, followed by a larger ‘target’ model which checks/corrects the entire batch in one forward pass. DeepSpec includes implementations of three training methods (DSpark, DFlash, and Eagle3), a data pipeline for constructing cached datasets, and an evaluation framework for evaluating on GSM8K, MATH, and HumanEval datasets. They also provide pre-trained draft checkpoints for the Qwen3 and Gemma target models, which have been validated on 8 GPU systems.
Why a vibe-coder should care
Using speculative decoding can reduce your inference costs if you have a local LLM instance or are paying per token on a cloud API. Since they also provide pre-trained draft checkpoints, you can get started right away by swapping in the draft checkpoints for Qwen3/Gemma models.
▌ More finds