Intermediate LevelAI, LLMs, Embeddings & MCP
What is Speculative Decoding and how does a small draft model accelerate large model inference?
❓ Interview Prompt: Explain small draft model token proposals, parallel validation by target models, and loss-less acceleration.
Large models (e.g. 70B parameters) are memory-bandwidth bound during token generation. Speculative Decoding accelerates inference without losing any mathematical accuracy.
Execution Steps:
1
Draft Phase: A fast, small draft model (e.g. 1B params) generates $K$ candidate tokens sequentially (e.g. 5 tokens).
2
Verification Phase: The large target model (70B) runs a single parallel forward pass over all $K$ candidate tokens simultaneously.
3
Accept / Reject: Target model accepts valid candidate tokens based on probability threshold criteria.
Key Benefit:
Generates multiple tokens per target model forward pass, achieving 2x to 3x speedup with zero loss in output quality!
Code Implementation Example
typescript
// Speculative Decoding Step Concept
async function speculativeStep(draftModel: Model, targetModel: Model, context: Token[]): Promise<Token[]> {
// 1. Draft model quickly proposes 4 candidate tokens
const candidates = await draftModel.generate(context, { tokens: 4 });
// 2. Target model validates all 4 candidate tokens in ONE forward pass
const acceptedTokens = await targetModel.verifyParallel(context, candidates);
return acceptedTokens;
}💡Draft model candidate generation and parallel target model verification.
🎯 Interview Key Takeaway
Speculative decoding uses a fast draft model to propose tokens validated in parallel by the target model, doubling inference speed.
#Speculative Decoding#Inference#Latency#Intermediate
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