Speculative Decoding: The Free Speed Toggle Your Local LLM Is Probably Not Using (vettedconsumer.com)

A new setting called speculative decoding, now gaining traction in local language models (LLMs) like LM Studio and llama.cpp, enables significant speed improvements in text generation. By utilizing a smaller draft model to predict multiple tokens simultaneously, the primary model can increase its output speed by 1.5 to 2.5 times without compromising the quality or accuracy of the generated text. This process exploits the GPU's ability to handle parallel processing, enhancing performance where token generation typically bottlenecks due to memory bandwidth limitations. While initially a niche technique among power users, advancements in 2026, particularly multi-token prediction (MTP), have made it accessible to everyday users, with built-in draft models included with LLMs like Qwen 3.6 and Gemma 4. The implications for the AI/ML community are substantial, especially for developers and researchers focused on optimizing model performance. The technique introduces a streamlined workflow, minimizing setup complexity and improving speed for coding, summarization, and extraction tasks. However, users should be aware that speculative decoding may not deliver benefits for creative writing or when the workload is pushed into VRAM constraints, making it essential to monitor acceptance rates to assess its effectiveness. As the adoption of speculative decoding becomes mainstream, it signals a shift toward enhancing computational efficiency in local LLM deployment, making high-performance AI tools more accessible to a broader audience.