Speculative Decoding: 2-3x Faster LLM Inference (2026)

Speculative decoding addresses the memory bandwidth issue encountered during large language model (LLM) inference by optimizing the process of generating multiple tokens in a single forward pass. This technique pairs a smaller draft model, which proposes several tokens quickly, with a larger target model that verifies the tokens in parallel, effectively reducing the number of sequential passes required. Introduced by Google and now widely adopted in AI frameworks like vLLM and SGLang, speculative decoding can offer a 2-3x speedup, particularly in low-concurrency scenarios, by enhancing the utilization of modern GPUs that otherwise sit idle due to memory latency. The success of speculative decoding hinges on the acceptance rate of the draft model's proposals by the target model, which varies by task type, with predictable tasks achieving higher acceptance rates. Various approaches to speculative decoding exist, such as using external draft models or EAGLE-style draft heads, each with its own trade-offs in terms of setup complexity, memory overhead, and speedup potential. Speculative decoding is particularly beneficial in interactive applications, but its effectiveness diminishes in high-throughput batch processing or when dealing with highly creative or domain-specific content without a well-matched draft model.