Inference with modern Large Language Models (LLMs) is expensive and time-consuming,
and speculative sampling has proven to be an effective solution. Most speculative sampling …

Speculative decoding has emerged as a widely adopted method to accelerate large
language model inference without sacrificing the quality of the model outputs. While this …

Speculative decoding accelerates inference in large language models (LLMs) by generating
multiple draft tokens simultaneously. However, existing methods often struggle with token …

Due to the high resource demands of Large Language Models (LLMs), achieving
widespread deployment on consumer-grade devices presents significant challenges …

The growing scale of Large Language Models (LLMs) has exacerbated inference latency
and computational costs. Speculative decoding methods, which aim to mitigate these issues …

Web-based large language model (LLM) has garnered significant attention from both
academia and industry due to its potential to combine the benefits of on-device computation …

Speculative decoding has emerged as a widely adopted method to accelerate large
language model inference without sacrificing the quality of the model outputs. While this …

Speculative decoding has emerged as a critical technique for accelerating inference in large
language models, achieving significant speedups while ensuring consistency with the …