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amazon/
gpt-oss-120b-p-eagle-long-context

Name: gpt-oss-120b-p-eagle-long-context
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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.

For use cases of less than 10k context length - please consider using gpt-oss-120b-P-Eagle.

Model Details

The model architecture is illustrated in the following figure. Specifically, we trained a 4-layer P-EAGLE for GPT-OSS 120B as the target model, with number of parallel-token prediction as 8.

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 120B

Model Sources

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

Training Data

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 openai/gpt-oss-120b \
  --speculative-config '{"method": "eagle3", "model": "amazon/gpt-oss-120b-p-eagle-long-context", "num_speculative_tokens": 5, "parallel_drafting": true}' \
  --tensor-parallel-size 1 \
  --kv-cache-dtype fp8 \
  --async-scheduling \
  --stream-interval 20 \
  --max-cudagraph-capture-size 4096 \
  --no-enable-prefix-caching \
  --port 8040 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 128 \
  --max-model-len 131072

Evaluation

From vllm-bench, with speculation length of 5 and max-new-token of 2048, we see the following acceptance length for gpt-oss-120b-P-Eagle and its long-context extension (the current model).

Benchmark	gpt-oss-120b-P-Eagle	long context extension
aa-lcr (100k)	1.48	2.42
humaneval	3.05	2.95
mtbench	2.57	2.50

The command to run benchmarking is shown as below.

vllm bench serve \
    --backend openai-chat \
    --base-url http://localhost:8040 \
    --endpoint /v1/chat/completions \
    --model openai/gpt-oss-120b \
    --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}
}