vLLM splits multimodal inference into independently scaled EPD stages

1. 5:03 AM
   vLLM@vllm_projectOfficial

   vLLM details engineering behind independent encoder and language-model scaling

   AIvLLM says its new blog covers engineering improvements, including metadata-only requests that avoid duplicate preprocessing and Mooncake/NIXL embedding transfers. The post also cites cross-encoder caching and EPD integration with vLLM Router, SMG, and llm-d for independent scaling of encoder and language-model instances.

2. 5:03 AM
   vLLM@vllm_projectOfficial

   vLLM cuts DeepSeek V4.1 Flash multi-page document TTFT by 44–47%

   AIvLLM says its latest EPD disaggregation improvements, covering data transfer, cache reuse, and routing, cut P90 time to first token by 44–47% in a DeepSeek V4.1 Flash multi-page document benchmark at concurrency 64–128. The same benchmark shows token throughput per GPU rising 6.6–9.5%, counting every encoder GPU.

3. 5:03 AM
   vLLM@vllm_projectOfficial

   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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