AWS has introduced P-EAGLE, a parallel speculative decoding approach designed to improve large language model inference performance on Amazon SageMaker AI.
Unlike traditional EAGLE implementations that generate draft tokens sequentially, P-EAGLE produces multiple draft tokens in a single forward pass, eliminating a major inference bottleneck.
Integrated into vLLM, the technique delivers up to 1.69x faster performance compared to EAGLE-3 on real-world workloads running on NVIDIA B200 GPUs. AWS has also released pre-trained P-EAGLE checkpoints for models including GPT-OSS and Qwen3-Coder, enabling developers to accelerate inference, increase throughput, and optimize production AI deployments more efficiently.
