# Accelerating decode-heavy LLM inference with speculative decoding on AWS Trainium and vLLM

Canonical URL: https://www.traeai.com/articles/44a5b147-bdca-4975-b22f-bbfcd4798188
Original source: https://aws.amazon.com/blogs/machine-learning/accelerating-decode-heavy-llm-inference-with-speculative-decoding-on-aws-trainium-and-vllm/
Source name: AWS Machine Learning Blog
Content type: article
Language: 未知
Score: 8.5
Reading time: 未知
Published: 2026-04-15T15:20:58+00:00
Tags: 投机解码, LLM推理优化, AWS Trainium, vLLM

## Summary

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## Key Takeaways

- 投机解码通过小模型预生成多Token并由大模型单次验证，显著降低KV Cache内存往返次数，提升硬件利用率。
- 需选择同词表且架构相近的草稿模型以保证高接受率，并通过调节num_speculative_tokens平衡草稿计算与验证开销。
- 在AWS Trainium2结合vLLM部署模型时，该技术可使解码密集型负载的Token间延迟降低最高达3倍。

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