vllm

Separate generation, scoring, learning and policy publication. Size the pools from matched workload measurements, and make policy versions explicit before overlapping rollout generation with updates.

One request finishes, another keeps writing, and a third waits outside. Follow the same workload through two schedules to see what continuous batching improves, and what it cannot promise.

Trace one cache position through a block table, calculate the memory recovered from oversized reservations, and explain why sharing still needs careful writes and matching.

Filter engine candidates by exact compatibility, then compare correctness, goodput and operational cost on the same workload.

Derive a deployment candidate from the official tensor index, then account for latent-cache replication and the product workload. A worked example shows why enough aggregate HBM can still leave every GPU out of memory.

Find which startup step runs out of memory, count the allocations on that GPU, and change the setting that controls the shortage.

Find whether expert placement, small batches or communication makes the deployment slower. Compare equivalent workloads and inspect the worker that holds up each step.

Measure reachable prefix reuse, separate token savings from latency, and compare both engines on the same agent contract and resource budget.

Use the actual tensor index to size Kimi K3, then separate a published hardware starting point from production capacity. Account for mixed precision, hybrid recurrent state, parser behavior, and the workload behind a throughput claim.

Derive a prompt-plus-output contract, calculate per-rank state capacity, and measure the concurrency that meets latency and overload targets.

The nodes joined the cluster, but the first request hangs. Trace how far it got, check worker assignments, and reproduce the blocked operation on the same communication path.

Compare committed output per complete draft-and-verify cycle with ordinary decoding. Derive the acceptance-length model, separate it from engine counters, and test latency, goodput and memory across the actual workload.

Map a checkpoint format to compute capability, then say which part of the speedup survives on each generation. The same file can load on two GPUs and take two different execution paths.

Turn a benchmark number into a bill that survives real utilization, then write the serving contract the gateway enforces and the evaluation the tuned configuration still owes.

A measured run shows where the time goes in a small batch job, then why every result parsed but none was deliverable. The answer turns on startup cost, co-tenant latency and a per-item contract.

Walk a single-GPU vLLM Deployment from pod scheduling to the first token, then show the three settings that turn a working manifest into restart loops and stuck rollouts.

Same model, same engine name, different execution model. Trace what moves into compilation, how shape buckets trade compile time for padding, and how per-device memory decides the slice.