The quest for efficiency in large language model serving leads to the development of speculative decoding, a method that promises faster text generation. The Recurrent Drafter incorporates the benefits of both two-model and single-model speculative decoding strategies, introducing a lightweight recurrent draftsman for efficient decoding.

• Employs a single-model method to avoid complexities of transformer architectures.
• Uses beam search for swift elimination of unsuitable drafts.
• Demonstrates effectiveness across several open-source LLMs.
• Presents a trade-off analysis of adopting this approach.

The Recurrent Drafter’s approach to speculative decoding simplifies the generation process, offering a balance between efficiency and performance that could propel the employment of LLMs in real-time applications.