# Prefix caching: when repeated context helps a GPU pool

How to evaluate reuse of a shared LLM request prefix, and why selecting a GPU requires separate cold-cache and warm-cache tests.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/prefix-caching/
Language: en

Many applications begin requests with the same instructions, tool descriptions or shared document. Reprocessing everything repeats some computation. GPU owners should therefore discuss expected request shape as well as device capability.

## What is reused?

The [vLLM Automatic Prefix Caching documentation](https://docs.vllm.ai/en/stable/features/automatic_prefix_caching/) describes reusing KV cache for a shared request prefix. This is not a library of prepared answers: an identical beginning can reduce repeated context processing, after which the model continues the new request.

This limits benchmark interpretation. Repeating one instruction many times measures highly repetitive traffic. It does not automatically characterize independent documents without a shared beginning.

## Compare three modes

Suppose a team wants to host an assistant with lengthy common rules. A pilot can use this structure:

| Mode | What repeats | Why measure it |
| --- | --- | --- |
| Cold | Nothing prewarmed | Observe the first request |
| Working session | Shared rules, different questions | Test useful repetition |
| Different documents | Little shared context | Assess another traffic mix |

Obtain figures through measurement rather than assigning them beforehand. Save model, chat template and tokenization parameters before choosing a mode, or shared-prefix comparisons may be invalid.

## Check memory cost

Reusing computation does not make resources free. The server must hold necessary data in available memory. Cache presence matters, but so does its coexistence with active requests.

Test several independent sessions with different large prefixes. Then add ordinary short requests. If behavior changes substantially, describe the dependency in the workload specification.

Owners should know which scenario produced the best result. This prevents both parties from expecting one successful warmup to become a permanent hardware property.

## Account for restarts and model changes

Maintenance and updates may require return to the working state. Include loading time and initial requests. Test switching model or message-template versions separately.

Do not call cache reuse guaranteed savings for the whole month. Results depend on request mix, resources and service organization. This is an engineering opportunity to test rather than a financial-contract rate.

For reporting, record reused-context share alongside latency. It then becomes easier to distinguish workload changes from GPU or network faults.

## Assessment notes

Instead of configuring every mode independently, owners can offer equipment and discuss a suitable pilot with the team. The initial task is a clear configuration and availability windows.

Also see [LLM VRAM](/journal/vram-and-models/), [benchmark methodology](/journal/inference-benchmark-method/) and [equipment usage accounting](/journal/capacity-reporting/).