Speculative Sampling

Speculative sampling aims to accelerate the slow decoding process in large language models (LLMs) by predicting multiple tokens concurrently, rather than one at a time. Current research focuses on improving the accuracy and efficiency of these predictions, exploring techniques like dynamic draft trees that adapt to contextual information, optimized parallel processing on GPUs, and batched sampling for multiple sequences. These advancements significantly reduce inference latency and increase throughput, making LLMs more practical for real-world applications requiring rapid text generation.