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Written by Max Zeshut
Founder at Agentmelt
An inference optimization technique where a small, fast 'draft' model generates candidate tokens ahead of the main model, and the main model verifies them in parallel. When the draft model's predictions match what the main model would have produced, tokens are accepted instantly—reducing latency by 2–3x without changing output quality. Speculative decoding is particularly valuable for AI agents where response latency directly affects user experience, especially voice agents and live-chat support agents.