vLLM separates multimodal inference stages so each can scale independently

AIvLLM's EPD design splits multimodal inference into encoding, prefill, and decode stages that scale independently, so adding images does not require scaling the entire language-model tier. The post says DeepInfer also uses EPD disaggregated deployment to allocate resources flexibly across those stages.

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Multimodal workloads don’t scale uniformly. More images shouldn’t mean scaling the entire language-model tier.

EPD in vLLM separates encoding, prefill, and decode so each stage can scale independently. DeepInfer also uses EPD disaggregated deployment to allocate resources flexibly across stages.

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vLLM · x.comPublished Nov 11, 5:03 AM ET · added here 5:15 AM ET

Original title: Multimodal workloads don’t scale uniformly. More images shouldn’t mean scaling the entire language-model tier.

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Multimodal AI Deployment & engineering Tutorials & how-to