Batch Speculative Decoding Done Right
PositiveArtificial Intelligence
A recent study on speculative decoding highlights its potential to enhance large language model (LLM) inference by using a draft model to propose multiple tokens for verification. This approach, particularly when applied to batches, is crucial for efficient production serving. However, it also presents challenges, such as the ragged tensor problem, which can disrupt the alignment of sequences within a batch. Addressing these issues is vital for improving the performance and reliability of LLMs in real-world applications.
— Curated by the World Pulse Now AI Editorial System


