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Speculative Decoding in vLLM: Complete Guide to Faster LLM Inference

· 34 min read
Jaydev Tonde
Jaydev Tonde
Data Scientist

Speculative Decoding vLLM Cover

Introduction​

Ever waited for an AI chatbot to finish its answer, watching the text appear word by word slow? It can feel painfully slow, especially when you need a fast response from a powerful Large Language Model (LLM).

The big problem is in how LLMs generate text. They don't just write a paragraph all at once; they follow a strict, word-by-word approach.

  1. The model looks at the prompt and the words it has generated so far.
  2. It calculates the best next word (token).
  3. It adds that word to the text.
  4. It repeats the whole process for the next word.

Each step involves complex calculations, meaning the more text you ask for, the longer the wait. For developers building real-time applications (like chatbots, code assistants, or RAG systems), this slowness (high latency) is a major problem.