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.
AIConfigurator: Lightning-fast configuration optimization for multi-framework LLM serv- ing
7 Pith papers cite this work. Polarity classification is still indexing.
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Frontier is a new discrete-event simulator for disaggregated LLM serving that incorporates co-location, PDD, AFD, and optimizations, achieving under 4% throughput error and large reductions in latency prediction error versus prior simulators.
LLM-Emu is a serving-native emulator for vLLM that replaces GPU execution with profile-driven latency sampling and achieves under 5% error on TPOT, ITL, E2E latency, and throughput across multiple models, GPUs, and workloads.
LLM serving should triage by five-resource analytical floors and wall ordering, not grid search; on 16×H20, TP16 is capacity-capped at ~70 while EP+DP attention reaches ~644 concurrent 8K requests.
KernelSight-LM simulates LLM inference at kernel granularity with cross-generation (12.1% per-kernel error) and target-measured (3.8% error) tiers, yielding end-to-end median errors of 15.4%/12.8%/3.0% and 14.3%/6.2%/2.7% for TTFT/TPOT/throughput across six model families.
Operator-level attention-FFN disaggregation enables ~4k tokens/s throughput for DeepSeek-V3.2 under tight TTFT/TPOT SLOs where chunked-prefill and prefill-decode baselines cannot.
Workload-aware optimizations for LLM serving in AML and fraud detection yield substantial gains in throughput, latency, and GPU utilization on synthetic compliance prompts.
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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Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
Frontier is a new discrete-event simulator for disaggregated LLM serving that incorporates co-location, PDD, AFD, and optimizations, achieving under 4% throughput error and large reductions in latency prediction error versus prior simulators.
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LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Sampling
LLM-Emu is a serving-native emulator for vLLM that replaces GPU execution with profile-driven latency sampling and achieves under 5% error on TPOT, ITL, E2E latency, and throughput across multiple models, GPUs, and workloads.
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Think Before You Grid-Search: Floor-First Triage for LLM Serving
LLM serving should triage by five-resource analytical floors and wall ordering, not grid search; on 16×H20, TP16 is capacity-capped at ~70 while EP+DP attention reaches ~644 concurrent 8K requests.
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KernelSight-LM: A Kernel-Level LLM Inference Simulator
KernelSight-LM simulates LLM inference at kernel granularity with cross-generation (12.1% per-kernel error) and target-measured (3.8% error) tiers, yielding end-to-end median errors of 15.4%/12.8%/3.0% and 14.3%/6.2%/2.7% for TTFT/TPOT/throughput across six model families.
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How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
Operator-level attention-FFN disaggregation enables ~4k tokens/s throughput for DeepSeek-V3.2 under tight TTFT/TPOT SLOs where chunked-prefill and prefill-decode baselines cannot.
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Rethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack
Workload-aware optimizations for LLM serving in AML and fraud detection yield substantial gains in throughput, latency, and GPU utilization on synthetic compliance prompts.