This research introduces lossless speculative decoding methods that eliminate vocabulary matching requirements between drafter and target models, significantly enhancing LLM inference speed.

• Removes the constraint that drafter and target models must share the same vocabulary
• Enables using a wider range of existing pre-trained models as drafters
• Eliminates the need to train drafters from scratch
• Maintains the quality of outputs while improving inference efficiency

For engineering teams, these algorithms provide practical solutions to a critical bottleneck in generative AI deployment, allowing more flexible and cost-effective acceleration of LLM inference in production environments.

Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies