Nightjar: Adaptive Speculative Decoding for LLM Serving
Analysis
This paper addresses a key limitation of speculative decoding (SD) for Large Language Models (LLMs) in real-world serving scenarios. Standard SD uses a fixed speculative length, which can hurt performance under high load. Nightjar introduces a learning-based approach to dynamically adjust the speculative length, improving throughput and latency by adapting to varying request rates. This is significant because it makes SD more practical for production LLM serving.
Key Takeaways
- •Nightjar is a learning-based algorithm for adaptive speculative inference.
- •It dynamically adjusts the speculative length based on request load.
- •It can disable speculative decoding when it provides no benefit.
- •Achieves higher throughput and lower latency compared to standard SD.
Reference
“Nightjar achieves up to 14.8% higher throughput and 20.2% lower latency compared to standard speculative decoding.”