Introduction
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:
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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.
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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.
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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×.
Model Weights
| Model Series | Model Name | Download |
|---|---|---|
| llama3 | PARD-Llama-3.2-1B | 🤗 HuggingFace |
| DSR Qwen | PARD-DeepSeek-R1-Distill-Qwen-1.5B | 🤗 HuggingFace |
| Qwen | PARD-Qwen2.5-0.5B | 🤗 HuggingFace |
How To Use
Please visit PARD repo for more information
Citation
@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}
}