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Core
Continuous Batching
GPUs are efficient on batches, but LLM requests arrive at different times and finish after different numbers of tokens, so static batching wastes the GPU waiting for the slowest request. Continuous (in-flight) batching adds and removes requests from the running batch at each decoding step, keeping the GPU full and dramatically raising throughput. Applied-AI interviews probe it because it is the single biggest throughput lever in LLM serving and explains why one replica can serve many concurrent users.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
ML Infrastructure & GPUsCompare static, dynamic, and continuous batching for LLM serving and state the tradeoffs.→ML Infrastructure & GPUsHow does continuous (in-flight) batching improve LLM serving throughput vs static batching?→ML Infrastructure & GPUsExplain speculative decoding and the other main levers for cutting LLM generation latency.→System Design for AI in ProductionDesign an LLM inference platform (vLLM-as-a-service) serving many models and teams.→ML Infrastructure & GPUsYour inference p50 is fine but p99 latency spikes under load. How do you fix tail latency?→ML Infrastructure & GPUsHow do you load-test and capacity-plan an LLM inference service before launch?→
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