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 GPT-OSS 20B as the target model, with number of parallel-token prediction as 10.

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: GPT-OSS 20B

Model Sources

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

Training Data

• 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:

Note: GPT-OSS 20B uses hybrid attention (sliding window + full attention). When combined with the P-EAGLE drafter, a KV cache grouping fix is required for vLLM to correctly separate speculator layers into a dedicated KV cache group. Without this fix, vLLM will fail with a validate_same_kv_cache_group error. Apply the fix from the PR or use a vLLM version that includes it.

CUDA_VISIBLE_DEVICES=0 VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8=1 \
  vllm serve openai/gpt-oss-20b \
  --speculative-config '{"method": "eagle3", "model": "amazon/GPT-OSS-20B-P-EAGLE", "num_speculative_tokens": 7, "parallel_drafting": true}' \
  --tp 1 \
  --max-num-batched-tokens 32768 \
  --kv-cache-dtype fp8 \
  --async-scheduling \
  --stream-interval 20 \
  --max-cudagraph-capture-size 4096 \
  --no-enable-prefix-caching \
  --port 8050 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 128 \
  --max-model-len 32768

Evaluation

From vllm-bench, with max-new-token of 2048, concurrency 1, and temperature 0 on a single H200 GPU (MXFP4 weights, FP8 KV cache):

Acceptance Length

Throughput (output tok/s, concurrency=1)

The command to run benchmarking is shown as below.

vllm bench serve \
    --backend openai-chat \
    --base-url http://localhost:8050 \
    --endpoint /v1/chat/completions \
    --model openai/gpt-oss-20b \
    --dataset-name custom \
    --dataset-path /home/ubuntu/eval_datasets/humaneval_custom.jsonl \
    --custom-output-len 2048 \
    --num-prompts 164 \
    --max-concurrency 1 \
    --request-rate inf \
    --temperature 0 \
    --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}
}