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TIDE introduces a serving-engine-native framework that leverages intermediate hidden states from inference to train draft models, eliminating the need for separate target model computations during adaptation. By dynamically activating speculation and training only when beneficial, it achieves significant throughput gains on heterogeneous GPU clusters while drastically reducing storage and training time requirements. This approach effectively addresses the challenge of evolving workloads where static draft models often degrade performance.
TIDE: Temporal Incremental Draft Engine for Self-Improving LLM Inference
arXiv:2602.05145v2 Announce Type: replace Abstract: Speculative decoding can substantially accelerate LLM inference, but realizing its benefits in practice is challenging due to evolving workloads. We present TIDE (Temporal Incremental Draft Engine), a serving-engine-native framework that integrates online draft adaptation directly into high-performance LLM inference systems. TIDE reuses target model's intermediate hidden states generated during inference as training signals for draft adaptation, thereby avoiding additional target model computation and serving-time overhead. It employs adaptive runtime control to activate speculation and draft model training only when beneficial. TIDE exploits heterogeneous clusters by mapping inference and training to appropriate GPU classes. Across diverse real-world workloads, TIDE achieves up to 1.66$\times$ throughput over no-speculation baselines while recovering performance on misaligned workloads where static draft models degrade throughput. TIDE also reduces training time by up to 3.02$\times$ and storage requirements by 24$\times$ compared to existing draft training approaches, and improves system throughput by up to 1.22$\times$ on heterogeneous GPU clusters.
Abstract:Speculative decoding can substantially accelerate LLM inference, but realizing its benefits in practice is challenging due to evolving workloads. We present TIDE (Temporal Incremental Draft Engine), a serving-engine-native framework that integrates online draft adaptation directly into high-performance LLM inference systems. TIDE reuses target model's intermediate hidden states generated during inference as training signals for draft adaptation, thereby avoiding additional target model computation and serving-time overhead. It employs adaptive runtime control to activate speculation and draft model training only when beneficial. TIDE exploits heterogeneous clusters by mapping inference and training to appropriate GPU classes. Across diverse real-world workloads, TIDE achieves up to 1.66$\times$ throughput over no-speculation baselines while recovering performance on misaligned workloads where static draft models degrade throughput. TIDE also reduces training time by up to 3.02$\times$ and storage requirements by 24$\times$ compared to existing draft training approaches, and improves system throughput by up to 1.22$\times$ on heterogeneous GPU clusters.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.05145 [cs.LG]
(or arXiv:2602.05145v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.05145 Focus to learn more arXiv-issued DOI via DataCite
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