Advanced

Chunked Prefill

Follow a long prompt through a shared token budget, including its final partial chunk. Learn why chunking can reduce stream pauses without guaranteeing a fixed token gap or a universal throughput gain.

This concept is part of Premium, which includes all concepts, course lessons and answers.

Get Premium · ₹2,000 / $256 months of access · one payment · no auto-renew

RELATED CONCEPTS

Prefill vs Decode Continuous Batching Latency Metrics: TTFT, TPOT and Goodput Disaggregated Prefill and Decode Designing for Latency SLOs Memory-Bound vs Compute-Bound Kernels

LESSONS THAT TEACH THIS

Foundations of AI InfrastructureA request becomes tokens, and tokens become two different workloads30 min→Foundations of AI InfrastructureDefine when requests join, leave and wait13 min→Inference EngineeringPrefix reuse saves prompt work and shared storage when state matches14 min→

PRACTICE THIS IN REAL QUESTIONS

LLM Inference & ServingA long prompt arrives while sixty users are mid-generation. What happens, and how does chunked prefill fix it?→Open-Weights Models & Serving EnginesYour p99 time to first token is four times p50. Find out why.→LLM Inference & ServingWhy do prefill and decode behave so differently, and why does that matter for the hardware you serve on?→LLM Inference & ServingDesign the fastest serving path for a 70B model with a 200 ms TTFT target at production scale.→LLM Inference & ServingDefine TTFT, TPOT and goodput, and tell me how you would measure each one in production.→LLM Inference & ServingWhat is the difference between static and continuous batching, and why did it change LLM serving?→