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Continuous Batching

Static batching runs a fixed group of requests to completion together, so a batch of one short reply and one long reply makes the GPU idle while it waits on the longest. Continuous batching adds and evicts sequences from the running batch every decode step, keeping the GPU saturated and multiplying throughput. It is the scheduling trick at the heart of vLLM and every modern LLM serving stack.

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RELATED CONCEPTS

Inference Serving (vLLM, TGI)KV Cache PagedAttention Latency Optimization

LESSONS THAT TEACH THIS

Hard DeploymentsMeasure throughput and user latency before choosing a batching policy30 min→Hard DeploymentsMeasure throughput without losing the requests that failed180 min→Hard DeploymentsTest the model server's cache, adapter and shared-failure boundaries30 min→

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWalk me through what actually happens when an LLM generates a response.→MLOps & ML EngineeringWhat problem does a feature store solve? Explain the online store versus the offline store.→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?→LLM & GenAI FundamentalsA customer says your LLM app is too slow. Give me five levers to reduce latency, and their tradeoffs.→ML Infrastructure & GPUsWhat is the KV cache, why does it dominate serving memory, and how do you size it? Do the math for a 70B model.→