Turning Memory Bottlenecks into Speedup: How Speculative Decoding Transforms LLM Inference

Modern LLM inference is fundamentally constrained by memory bandwidth rather than compute capacity—moving model weights and KV-cache dominates latency even when GPUs sit idle. Speculative decoding solves this by having a smaller draft model generate multiple token proposals while a single forward pass of the target model verifies them, maintaining identical output distributions. In production deployments, this technique has achieved 2.7x latency reduction (380ms to 140ms) on the same hardware without quality trade-offs, making it critical for cost-efficient LLM serving at scale.