P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM | Amazon Web Services

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• 来源: blogs_podcasts
• 原始来源: https://aws.amazon.com/blogs/machine-learning/p-eagle-faster-llm-inference-with-parallel-speculative-decoding-in-vllm

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EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that you speculate, the more sequential forward passes the drafter needs. Eventually those overhead eats into your gains. P-EAGLE removes this ceiling by generating all K draft tokens in a single forward pass, delivering up to 1.69x speedup over vanilla EAGLE-3 on real workloads on NVIDIA B200.

You can unlock this performance gain by downloading (or training) a parallel-capable drafter head, adding “parallel_drafting”: true on you vLLM serving pipeline. Pre-trained P-EAGLE heads are already available on HuggingFace for GPT-OSS 120B , GPT-OSS 20B , and Qwen3-Coder 30B , so you can start today.…

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