vLLM model length

Set vLLM max model length from the KV Cache budget

In vLLM, maximum model length is not just a model capability: it is a capacity reservation for prompt and output tokens across active requests.

Start from the useful request envelope

Define the longest prompt-plus-output path your endpoint needs to complete. Set the model-length target from that workload envelope, not from the model card maximum by default.

Make the reservation visible

After loaded weights and runtime headroom, the remaining GPU memory becomes the KV Cache budget. At a fixed model length, that budget determines how many full-length requests can coexist.

Test the boundary in the serving planner

Enter the proposed model length and target request count together. If cache pressure is tight, lower one of those two requirements before changing the GPU target or tensor-parallel shape.

Continue planning

Test this memory constraint with a live capacity plan.

Plan max model length

Serving capacity

Plan context length and concurrency together

vLLM concurrency

Plan vLLM concurrency before raising max_num_seqs

Editorial record

Maintained by Make Your Own Tools to turn “Fit an LLM workload” into a defined weights, context or activations, runtime reserve, and the next hardware boundary. The references below provide the technical context for this planning path. Its calculation rules and planning assumptions are documented in the methodology, and affected calculations pass regression checks before the review date advances.

Last reviewed
Evidence set
4 primary references
Calculation coverage
2 affected rules checked
Read methodology →View change history ↗
Planning scope
vLLM maximum-model-length planning against a defined KV Cache budget.
Next check
Carry the target into the exact model configuration, vLLM release, parallelism shape, and observed request envelope.
vLLM cache configurationGPU memory utilization, KV Cache capacity, and per-engine concurrency at the configured model length.Open source ↗vLLM model configurationHow max model length is derived or set as a capacity boundary.Open source ↗vLLM parallelism and scalingTensor-parallel sizing for multi-GPU inference and serving.Open source ↗vLLM serve CLI referenceCurrent serve-argument reference monitored against the version-pinned runtime export contract.Open source ↗