Qwen3-Coder-30B-A3B-Instruct-P-EAGLE

Model Overview

P-EAGLE is a parallel-drafting speculative decoding model that generates K draft tokens in a single forward pass. It transforms EAGLE—the state-of-the-art speculative decoding method—from autoregressive to parallel draft generation.

Model Details

The model architecture is illustrated in the following figure. Specifically, we trained a 4-layer P-EAGLE for Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 as the target model, with number of parallel-token prediction as 18.

P-EAGLE follows the vanila EAGLE 3 using three layers of hidden states from the target model.

<img src="https://cdn-uploads.huggingface.co/production/uploads/64ab5fe189aa67e4a251b6b4/UBBMgZvXkOduu_LpUunQy.png" width="50%">

Model Description

• Developed by: AWS
• Model type: EAGLE
• Language(s) (NLP): English
• License: Apache License 2.0
• Target model: Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Model Sources

• Paper: P-EAGLE: Parallel-Drafting EAGLE with Scalable Training

Training Data

• nvidia/OpenCodeInstruct 200K
• Ultrachat_200k

Similar to nvidia/gpt-oss-120b-Eagle3-long-context: only prompts from the datasets were used for data synthesis (the original responses from GPT were not used for data synthesis) which is then used to train the P-Eagle.

Usage

To serve the checkpoint in vLLM

vllm serve \
--model Qwen/Qwen3-Coder-30B-A3B-Instruct \
--tensor-parallel-size 1 \
--max-model-len 16384 \
--speculative-config '{"method": "eagle3", "model": "amazon/Qwen3-Coder-30B-A3B-Instruct-P-EAGLE", "num_speculative_tokens": 10, "parallel_drafting": true}' \
--no-enable-prefix-caching \
--async-scheduling

Evaluation

From vllm-bench, the acceptance length (AL) on HumanEval dataset with different speculation length K is shown as below.

We use instruction-formatted prompts following standard practice for instruct models (similar to DeepSeek-Coder evaluation and Llama 3.1 8B instruction evaluation). The instruction we add in front of each prompt is Complete the following Python function. Only output the code, no explanations.

vLLM bench command is shown as below.

vllm bench serve \
      --backend openai-chat \
      --base-url http://localhost:8041 \
      --endpoint /v1/chat/completions \
      --model Qwen/Qwen3-Coder-30B-A3B-Instruct \
      --dataset-name custom \
      --dataset-path /home/ubuntu/eval_datasets/humaneval_qwen3coder_bench.jsonl \
      --custom-output-len 256 \
      --num-prompts 80 \
      --max-concurrency 1 \
      --temperature 0 \
      --request-rate inf \
      --save-result --save-detailed

Ciatation

@article{hui2026p,
  title={P-EAGLE: Parallel-Drafting EAGLE with Scalable Training},
  author={Hui, Mude and Huang, Xin and Salas, Jaime Campos and Sun, Yue and Pemberton, Nathan and Song, Xiang and Khetan, Ashish and Karypis, George},
  journal={arXiv preprint arXiv:2602.01469},
  year={2026}
}