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arXiv · 2404.19124

Accelerating Production LLMs with Combined Token/Embedding Speculators

Davis Wertheimer·Joshua Rosenkranz·Thomas Parnell·Sahil Suneja·Pavithra Ranganathan·Raghu Ganti·Mudhakar Srivatsa
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Abstract

This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production environment. By conditioning draft predictions on both context vectors and sampled tokens, we can train our speculators to efficiently predict high-quality n-grams, which the base model then accepts or rejects. This allows us to effectively predict multiple tokens per inference forward pass, accelerating wall-clock inference speeds of highly optimized base model implementations by a factor of 2-3x. We explore these initial results and describe next steps for further improvements.

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Davis Wertheimer, Joshua Rosenkranz, Thomas Parnell, Sahil Suneja, Pavithra Ranganathan, Raghu Ganti, Mudhakar Srivatsa. 2024-04-29. Accelerating Production LLMs with Combined Token/Embedding Speculators. https://arxiv.org/abs/2404.19124

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