Papers with plug-and-play LLM acceleration
UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware (2026.acl-long)
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| Challenge: | Existing methods for speculative decoding ignore device-specific verification costs and lack of mechanisms to assess draft token quality. |
| Approach: | They propose a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages. |
| Outcome: | The proposed framework outperforms existing training-free methods while maintaining identical output quality across different hardware environments. |