Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models
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Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models
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Speculative Streaming is a novel inference method designed to accelerate large language model (LLM) generation without the need for traditional auxiliary "draft" models. By integrating multi-stream attention directly into the target model, the system can perform future n-gram prediction and token verification simultaneously within a single forward pass. This approach eliminates the memory and complexity overhead of managing two separate models, making it exceptionally resource-efficient for hardware with limited capacity. The architecture utilizes tree-structured drafting and parallel pruning to maximize the number of tokens accepted per cycle while maintaining generation quality. Experimental results show speedups ranging from 1.8 to 3.1X across diverse tasks like summarization and structured queries. Ultimately, the method achieves performance comparable to more complex architectures while using significantly fewer additional parameters.
Source:
February 2024.Speculative Streaming: Fast LLM Inference without Auxiliary Models.Apple.Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, Mahyar Najibi.https://arxiv.org/pdf/2402.11131