SPIN introduces a novel approach to accelerate Large Language Model inference through advanced speculative decoding techniques using multiple specialized smaller models.

• Uses heterogeneous speculative models tailored to different request difficulties
• Implements dynamic batch processing that significantly improves throughput
• Achieves 2-4× faster inference speed compared to traditional approaches
• Provides a comprehensive framework for efficient token generation

This research matters for engineering teams because it directly addresses practical performance bottlenecks in LLM deployment, offering immediate throughput gains without compromising quality, potentially reducing compute costs and enabling more responsive AI applications.

SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models