← 🖥️ ML Infrastructure & ServingNEXT IN ML INFRASTRUCTURE & SERVINGDistributed Training: Parallelism and FSDP→
Core
Speculative Decoding
Decoding is sequential and memory-bound, so producing each token one at a time leaves the GPU underused. Speculative decoding runs a small, fast draft model to propose several tokens ahead, then the large model checks them all in a single parallel pass, keeping the longest correct prefix. It accelerates generation without altering output quality, since the big model still validates every token. AI, ML, and GenAI engineer interviews probe it because it is a clever, widely-used latency optimization that exploits the memory-bound nature of decode.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
ML Infrastructure & GPUsExplain speculative decoding and the other main levers for cutting LLM generation latency.→LLM & GenAI FundamentalsCompare speculative decoding variants: draft model, Medusa, EAGLE, and lookahead decoding.→MLOps & ML EngineeringYour model's p99 inference latency is too high. How do you bring it down without retraining?→MLOps & ML EngineeringHow do you optimize the cost of large-scale ML training and inference?→System Design for AI in ProductionDesign an LLM inference platform (vLLM-as-a-service) serving many models and teams.→ML Infrastructure & GPUsHow do you autoscale AI/LLM inference workloads, and why is it harder than autoscaling web services?→
COMPANIES THAT ASSUME THIS