Prefill-Decode Model Serving Report

Separating prefill from decode reshapes model serving. The analysis tracks resource use, latency, cache transfer, and operational complexity.

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Start with this prompt

A reproducible comparison of monolithic and split prefill-decode serving across explicit workloads, hardware, KV-cache paths, latency, reliability and cost.

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Task: As of 2026-07-01, determine when separating LLM prefill and decode improves serving over a monolithic design. Compare model sizes, prompt/output mixes, concurrency and KV-cache paths; assess TTFT, inter-token and tail latency, throughput, utilization, network overhead, reliability, complexity and total cost.

Research protocol: Define prefill, decode, disaggregation boundaries and every latency and throughput metric. Freeze model and quantization, framework release or commit, accelerator, interconnect, scheduler, batching, cache format, request traces and measurement window. Use current papers, framework docs and source, hardware guidance and reproducible tests; treat cloud or vendor benchmarks as claims unless their workload and environment can be matched. Run monolithic and split deployments on identical steady, mixed-length and burst traces, showing distributions rather than headline averages. Include KV-cache serialization and transfer, queueing, failure domains, autoscaling, observability and operator burden alongside accelerator cost. Explain version drift, incompatible metrics and missing evidence. Deliver an architecture map, experiment matrix, raw assumptions, comparable results, break-even conditions, operational risks and a workload-bound recommendation.

Stress-Test Burst Traffic

Changes the workload to bursts and tests when queueing, autoscaling and cache transfer improve or worsen tails.

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Task: As of 2026-07-01, determine when separating LLM prefill and decode improves serving over a monolithic design. Compare model sizes, prompt/output mixes, concurrency and KV-cache paths; assess TTFT, inter-token and tail latency, throughput, utilization, network overhead, reliability, complexity and total cost.

Research protocol: Define prefill, decode, disaggregation boundaries and every latency and throughput metric. Freeze model and quantization, framework release or commit, accelerator, interconnect, scheduler, batching, cache format, request traces and measurement window. Use current papers, framework docs and source, hardware guidance and reproducible tests; treat cloud or vendor benchmarks as claims unless their workload and environment can be matched. Run monolithic and split deployments on identical steady, mixed-length and burst traces, showing distributions rather than headline averages. Include KV-cache serialization and transfer, queueing, failure domains, autoscaling, observability and operator burden alongside accelerator cost. Explain version drift, incompatible metrics and missing evidence. Deliver an architecture map, experiment matrix, raw assumptions, comparable results, break-even conditions, operational risks and a workload-bound recommendation.

Plan an On-Premises Deployment

Changes the environment to a fixed on-premises fleet with network, power, security and staffing constraints.

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Task: As of 2026-07-01, determine when separating LLM prefill and decode improves serving over a monolithic design. Compare model sizes, prompt/output mixes, concurrency and KV-cache paths; assess TTFT, inter-token and tail latency, throughput, utilization, network overhead, reliability, complexity and total cost.

Research protocol: Define prefill, decode, disaggregation boundaries and every latency and throughput metric. Freeze model and quantization, framework release or commit, accelerator, interconnect, scheduler, batching, cache format, request traces and measurement window. Use current papers, framework docs and source, hardware guidance and reproducible tests; treat cloud or vendor benchmarks as claims unless their workload and environment can be matched. Run monolithic and split deployments on identical steady, mixed-length and burst traces, showing distributions rather than headline averages. Include KV-cache serialization and transfer, queueing, failure domains, autoscaling, observability and operator burden alongside accelerator cost. Explain version drift, incompatible metrics and missing evidence. Deliver an architecture map, experiment matrix, raw assumptions, comparable results, break-even conditions, operational risks and a workload-bound recommendation.

Prioritize Recovery and Observability

Changes the method to an operations-first failure study with SLOs, telemetry, fault injection and runbooks.

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Task: As of 2026-07-01, determine when separating LLM prefill and decode improves serving over a monolithic design. Compare model sizes, prompt/output mixes, concurrency and KV-cache paths; assess TTFT, inter-token and tail latency, throughput, utilization, network overhead, reliability, complexity and total cost.

Research protocol: Define prefill, decode, disaggregation boundaries and every latency and throughput metric. Freeze model and quantization, framework release or commit, accelerator, interconnect, scheduler, batching, cache format, request traces and measurement window. Use current papers, framework docs and source, hardware guidance and reproducible tests; treat cloud or vendor benchmarks as claims unless their workload and environment can be matched. Run monolithic and split deployments on identical steady, mixed-length and burst traces, showing distributions rather than headline averages. Include KV-cache serialization and transfer, queueing, failure domains, autoscaling, observability and operator burden alongside accelerator cost. Explain version drift, incompatible metrics and missing evidence. Deliver an architecture map, experiment matrix, raw assumptions, comparable results, break-even conditions, operational risks and a workload-bound recommendation.