PARD is a high-performance speculative decoding method that also enables low-cost adaptation of autoregressive draft models into parallel draft models. It offers the following advantages:
Low-Cost Training: PARD adapts AR (autoregressive) draft models into parallel draft models with minimal overhead. Compared to pure AR draft models, PARD achieves an average inference speedup of 1.78×. By introducing a conditional drop-token strategy, PARD improves training efficiency by up to 3× while maintaining the same level of accuracy.
Generalizability: Thanks to its target-independent design, a single PARD draft model can accelerate an entire family of target models. This contrasts with target-dependent approaches such as Medusa and EAGLE, which require retraining or tuning for each new target. As a result, PARD significantly reduces both deployment complexity and adaptation cost.
High Performance: When integrated into an optimized inference framework called Transformers+ PARD delivers up to a 4.08× speedup, with LLaMA3.1 8B reaches a state-of-the-art 311.5 tokens per second. When integrated into vLLM, PARD delivers up to 3.06× speedup, outperforming other speculative decoding methods in vLLM by 1.51×.
AR and AR+ represent baseline auto-regressive generation using Transformers and Transformers+, respectively. VSD denotes vanilla speculative decoding. PARD refers to the proposed method in this work.
@article{an2025pard,
title={PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation},
author={An, Zihao and Bai, Huajun and Liu, Ziqiong and Li, Dong and Barsoum, Emad},
journal={arXiv preprint arXiv:2504.18583},
year={2025}
}
