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Speculative Decoding

Decoding is slow because each token needs a full forward pass through a huge memory-bound model. Speculative decoding has a small fast draft model propose several tokens at once, then the large target model verifies them all in a single forward pass and keeps the longest correct prefix. A careful accept rule makes the output provably identical to sampling from the target model, so you get lower latency for free, not an approximation.

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RELATED CONCEPTS

Inference Serving (vLLM, TGI)KV Cache Latency Optimization

LESSONS THAT TEACH THIS

Hard DeploymentsMeasure throughput and user latency before choosing a batching policy30 min→

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWalk me through what actually happens when an LLM generates a response.→System Design & Production EngineeringWhat do p50, p95, and p99 latency actually mean, and why do production engineers obsess over the tail?→MLOps & ML EngineeringWhat problem does a feature store solve? Explain the online store versus the offline store.→System Design & Production EngineeringIt's 9am Monday and p99 latency is 10x normal. Walk me through your first 30 minutes.→AI Security, Privacy & GovernanceDesign safety guardrails into the request flow of an LLM serving system, where do they sit and what does each catch?→MLOps & ML EngineeringWhat is training-serving skew, and how do you keep online and offline features consistent?→