EAGLE-2: An Efficient and Lossless Speculative Sampling Method Achieving Speedup Ratios 3.05x – 4.26x which is 20% – 40% Faster than EAGLE-1
How do speculative sampling techniques improve LLM efficiency?
Speculative sampling techniques improve LLM efficiency by generating and verifying tokens in parallel, reducing latency. They leverage a smaller draft model to predict potential future tokens and a target LLM to verify these predictions. This approach speeds up the inference process without compromising the quality of generated text.
Why do LLMs face challenges with computational costs?
LLMs face challenges with computational costs due to their large size and complexity, which require substantial memory and processing power during inference. As these models increase, generating each token during autoregressive tasks becomes slower, impeding real-time applications. This high computational cost can lead to expensive storage and energy requirements, particularly when dealing with diverse tasks6.
What are the primary applications of large language models?
Large language models (LLMs) have various applications, including chatbots, translation services, and content creation. They are capable of understanding and generating human language, making them useful in domains such as natural language processing (NLP). LLMs can also be used for text summarization, sentiment analysis, and question-answering systems.