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Nightjar: Dynamic Adaptive Speculative Decoding for Large Language Models Serving

arXiv – CS AI|Rui Li, Zhaoning Zhang, Libo Zhang, Huaimin Wang, Xiang Fu, Zhiquan Lai|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Nightjar is a new adaptive speculative decoding framework for large language models that dynamically adjusts to system load conditions. It achieves 27.29% higher throughput and up to 20.18% lower latency by intelligently enabling or disabling speculation based on workload demands.

Key Takeaways

→Nightjar addresses the critical trade-off in speculative decoding where performance degrades under high-load conditions.
→The framework dynamically selects optimal speculative lengths for different batch sizes and can disable speculation when not beneficial.
→Memory optimization is achieved by offloading draft models to CPU under GPU memory pressure, allowing larger batch sizes.
→Performance improvements include 27.29% higher throughput and up to 20.18% lower latency compared to standard speculative decoding.
→The system uses a MAB planner to make real-time decisions about when speculation should be active or disabled.

#llm #speculative-decoding #inference-optimization #gpu-memory #batch-processing #throughput #latency #adaptive-systems

Read Original →via arXiv – CS AI

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