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Core
Speculative Decoding
Decoding is sequential and memory-bound, so generating each token one at a time underuses the GPU. Speculative decoding uses a small, fast draft model to propose several tokens ahead, then the large model verifies them all in a single parallel pass, accepting the longest correct prefix. It speeds up generation with no change to output quality, since the big model still validates every token. Applied-AI interviews probe it because it is a clever, widely-used latency optimization that exploits the memory-bound nature of decode.
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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?→
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