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Learning path
Breaking the Token Barrier: Exploring Speculative Decoding
Skill Level
Intermediate
Duration 40 minutes
Updated Nov 3, 2026
About this learning path
Why does a fast GPU still write tokens one at a time? This learning path explains the memory bottleneck behind LLM inference and how speculative decoding beats it without changing a single answer. Watch two short videos, read how draft, verify and accept works, then explore a real recorded run in a hands-on, on-demand lab.
Your instructors
Prerequisites
- A basic idea of what a large language model is. No GPU, coding or math background needed.
What you'll learn
- Explain why LLM generation speed is limited by memory, not compute
- Describe how a drafter and a target model work together in draft-and-verify cycles
- Explain why speculative decoding keeps the output unchanged
- Predict which workloads gain most from speculative decoding
- Explain why the speedup shrinks on a busy server