Disaggregated inference is modeled as three games whose price of anarchy rises at GPU saturation; an adaptive controller reduces the empirical PoA-hat by up to 3.1x on real clusters at modest throughput cost.
Prefill-Decode Aggregation or Disaggregation? Uni- fying Both for Goodput-Optimized LLM Serving.arXiv preprint arXiv:2508.01989, 2025a
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A load-aware prefill deflection scheduler for disaggregated LLM serving reduces P95 TTFT by up to 81% by interleaving chunked prefill on decode nodes and eliminating KV-cache transfers.
Layered prefill replaces token-chunked prefill with layer-group interleaving in MoE models, cutting TTFT by up to 70%, end-to-end latency by 41%, and per-token energy by 22% while preserving stall-free TBT.
citing papers explorer
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The Price of Anarchy in Disaggregated Inference
Disaggregated inference is modeled as three games whose price of anarchy rises at GPU saturation; an adaptive controller reduces the empirical PoA-hat by up to 3.1x on real clusters at modest throughput cost.
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Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving
A load-aware prefill deflection scheduler for disaggregated LLM serving reduces P95 TTFT by up to 81% by interleaving chunked prefill on decode nodes and eliminating KV-cache transfers.
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From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill
Layered prefill replaces token-chunked prefill with layer-group interleaving in MoE models, cutting TTFT by up to 70%, end-to-end latency by 41%, and per-token energy by 22% while preserving stall-free TBT.