> ## Documentation Index
> Fetch the complete documentation index at: https://runinfra.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Vision-language optimization runbook

> Multimodal prefix cache, concurrency caps, sequence ceilings, and image-bearing benchmarks for vision-language deployments.

## Stack target

Vision-language optimization sweeps three serving variants (baseline, balanced, throughput) tuned for image-bearing traffic. Every variant runs the multimodal prefix cache and a capped concurrency limit so image-heavy requests do not OOM.

| Technique                                   | Trigger                           | Win                                              | Plan      |
| ------------------------------------------- | --------------------------------- | ------------------------------------------------ | --------- |
| Multimodal prefix cache                     | Repeated image or document inputs | Faster repeated-image prompts                    | All plans |
| Multimodal concurrency cap                  | All vision-language deployments   | OOM protection for image-heavy requests          | All plans |
| Per-family sequence ceiling                 | Known vision-language family      | Safer defaults by model size                     | All plans |
| Throughput tuning (batch + chunked prefill) | Bulk Q\&A or OCR pipelines        | Higher batched throughput within OOM-safe limits | All plans |
| Image-bearing benchmark requests            | Benchmark sweep                   | Measures real image-encoder cost                 | All plans |

## Public contract

Vision-language optimization uses vision-specific serving techniques. It does not present generic LLM serving work as runnable vision-language optimization unless the selected runtime can actually apply it to image-bearing requests.

RunInfra validates model family, runtime, image support, and benchmark evidence before promoting a vision-language variant.

## Detection, routing, and application

| Decision                 | Selection rule                   | Runtime behavior                                                                                                                                          |
| ------------------------ | -------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Vision-language modality | Pipeline model and request shape | Uses the vision-language optimization recipe                                                                                                              |
| Family detection         | Model architecture and config    | Chooses family-specific safety caps for Qwen-VL, LLaVA, Mllama, InternVL, Phi-Vision, Idefics, PaliGemma, Pixtral, Molmo, MiniCPM-V, and related families |
| Prefix cache             | Every vision-language variant    | Enables multimodal prefix caching on all variants                                                                                                         |
| Concurrency cap          | Model size and image load        | Caps concurrent multimodal requests to prevent OOM                                                                                                        |
| Throughput tuning        | Throughput variant               | Raises batch size and chunked prefill within OOM-safe sequence limits                                                                                     |
| Image-bearing benchmark  | Benchmark profile                | Measures prompt latency with real image inputs                                                                                                            |

## Verification

1. The optimization plan should name the vision-language technique and the selected image workload.
2. Benchmarks should include image-bearing prompts rather than text-only substitutions.
3. Runtime evidence should preserve multimodal cache, concurrency, and sequence-limit settings.
4. Quality checks should preserve vision-specific signals rather than collapsing them into generic text quality.

## Rollback

```ts theme={"dark"}
constraints.customerOverrides = {
  skipTechniques: ["multimodal_prefix_cache"],
};
```

Skipping multimodal prefix cache is rarely the right choice because it is normally quality-neutral. Use this override only when a deployment-specific issue requires it.

## Availability notes

* Per-family sequence ceilings are available for common vision-language families.
* The multimodal prefix cache, concurrency cap, and sequence ceiling apply on every plan. There is no separate paid-tier gate for vision-language serving knobs today.
* OCR-heavy workloads should keep the model at its native precision and rely on the throughput variant for batched gains, since aggressive batching can shift quality on document tasks.