To mitigate the high inference latency stemming from autoregressive decoding in Large
Language Models (LLMs), Speculative Decoding has emerged as a novel decoding …

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a
unique blend of opportunities and challenges. Although the field has expanded and is …

The inference process in Large Language Models (LLMs) is often limited due to the absence
of parallelism in the auto-regressive decoding process, resulting in most operations being …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Speculative decoding (SD) accelerates large language model inference by employing a
faster draft model for generating multiple tokens, which are then verified in parallel by the …

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 …

Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded,
resulting in high latency and significant wastes of the parallel processing power of modern …

Large language models are trained on massive scrapes of the web, which are often
unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such …

Auto-regressive decoding makes the inference of Large Language Models (LLMs) time-
consuming. We propose a simple framework, EAGLE (Extrapolation Algorithm for Greater …

Large Language Models (LLMs) have attracted extensive attention due to their remarkable
performance across various tasks. However, the substantial computational and memory …