vLLM upgrades EPD disaggregation for lower TTFT
EPD disaggregation moves encoder work onto separate GPUs, which changes the cost calculus for anyone serving vision-language models where image encoding otherwise stalls token generation. Teams benchmarking vLLM against SGLang or TensorRT-LLM should re-test TTFT under mixed multimodal traffic with this release. The llm-d and SMG integrations mean the split can be adopted incrementally on existing vLLM deployments.
The vLLM project announced an upgrade to its EPD (encoder-prefill-decode) disaggregation capabilities, building on DeepInfer's demonstration of EPD's value. The improvements deliver lower time-to-first-token (TTFT) and higher throughput per GPU, with the throughput measurement counting all encoder GPUs in the deployment.
The upgraded EPD stack is integrated with vLLM Router, SMG, and llm-d, meaning teams can route and schedule disaggregated serving components across separate GPU pools rather than co-locating encoding, prefill, and decode work. DeepInfer's prior work supplied the evidence base for splitting these phases onto dedicated hardware.
Disaggregated serving matters most for multimodal and vision-language workloads, where encoder cost skews performance. For teams running vLLM in production, the update promises better per-GPU economics without changing their inference framework.
vLLM EPD, now upgraded 🚀
DeepInfer demonstrated the value of EPD disaggregation. vLLM’s latest EPD improvements deliver lower TTFT and higher throughput per GPU, counting all encoder GPUs, integrated with vLLM Router, SMG, and llm-d!
Read more here!👇 https://t.co/1N03mbEXo0
This report is categorized under AITOP's "Products" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include vLLM, DeepInfer, EPD disaggregation, inference infrastructure.
Source attribution: curated from vLLM (@vllm_project).
