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

This article explains how speculative decoding on AWS Trainium accelerates LLM token generation by up to 3x for decode-heavy workloads, reducing inference costs.

• Speculative decoding uses a small draft model to propose multiple tokens, verified by target model in single forward pass
• Reduces sequential decode steps, lowering KV-cache memory round trips and improving hardware utilization
• AWS Neuron supports four speculation modes: vanilla, fused, EAGLE, and Medusa
• Effectiveness depends heavily on prompt structure; works best for structured, predictable outputs
• Benchmarks show ~15ms inter-token latency for structured prompts vs ~45ms baseline; minimal gains for open-ended text
• TTFT and prefill latency unchanged; gains come from reduced decode steps only
• Key tuning parameters: draft model selection and num_speculative_tokens (optimal range 5-15)
• Deployed on Trn2 instances with vLLM and Kubernetes; code samples provided in AWS Neuron EKS repository

Speculative decoding significantly improves latency and cost for predictable workloads like code generation and structured extraction, but offers minimal benefit for open-ended generation.