amazon/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.

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](HuggingFaceH4/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.

| K | Acceptance Length | |---|-------------------| | 4 | 4.30 | | 10 | 6.66 | | 18 | 7.51 |

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}
}