Yahav Biran

AWS and vLLM have demonstrated that speculative decoding on Trainium chips can accelerate token generation by up to 3x for decode-heavy LLM workloads. The technique uses a small draft model to propose multiple tokens at once, which a larger target model verifies in a single forward pass, reducing sequential decode steps and lowering per-token inference costs. The post provides benchmarks using Qwen3 models, practical tuning guidance for draft model selection and speculative token window sizing, and reproducible instructions for deployment on Kubernetes.