ELDR reduces median TPOT by 5.9-13.9% in PD-disaggregated MoE serving via expert signatures from prefill, K-means partitioning, and locality-band routing with KV-co-indexed signature cache.
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MoETuner: Optimized mixture of expert serving with balanced expert placement and token routing
15 Pith papers cite this work. Polarity classification is still indexing.
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ViBE co-optimizes expert placement with measured GPU performance variability in MoE inference to cut execution-time imbalance, delivering 14% better SLO attainment and up to 45% lower P90 TTFT.
Task-aware expert grouping derived from family-specific co-activation traces cuts average communication cost 31.39% versus task-agnostic baselines in multi-task MoE inference while maintaining Jain fairness near 1.0.
A greedy max-weight decomposition strategy for MoE all-to-all communication on photonic fabrics improves overlap efficiency and reduces compute overheads compared to BvN by bounding the number of matchings.
NanoCP introduces request-level dynamic context parallelism to decouple MoE communication from KV cache placement in hybrid data-expert parallel serving, reporting up to 3.27x higher request rates and 2.12x lower P99 latency under TPOT SLOs.
Expert-parallel scaling leaves per-expert routing imbalance flat; mock-token benchmarks overestimate real-text imbalance and fake a batch-size trend; architectures split into data-resilient (MHA, Mamba-2) and persistently concentrated (MLA, GDN) classes.
GEM is a GPU-variability-aware expert-to-GPU mapping framework for MoE inference that classifies experts as consistent or temporal and places them to equalize finish times across heterogeneous GPUs.
Hi-MoE uses two-level hierarchical routing objectives to enforce group-level balance while promoting within-group specialization, yielding better perplexity and expert utilization than prior MoE baselines in NLP and vision tasks.
FoE restructures MoE blocks into per-KV-head clusters with sum-based synchronization, removing all-to-all communication in single-node settings and limiting it to intra-node in multi-node settings for up to 5.2x faster inference with comparable quality.
SpaceMoE partitions MoE layers across orbiting satellite subnets in a ring and optimizes expert placement by activation probability and path latency, yielding at least 3x lower inference latency in thousand-satellite simulations versus random baselines.
Profiling shows persistent expert load imbalance and domain-specific activation patterns in large MoE models; workload-aware grouping and placement reduce all-to-all communication volume by up to 20x.
Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.
GRACE-MoE integrates expert grouping, dynamic replication, and locality-aware routing with hierarchical sparse communication to reduce end-to-end latency in distributed SMoE inference.
Replication reduces costs by 17-65% on average in hypergraph partitioning and 11-23% in DAG scheduling, sometimes eliminating communication needs entirely.
Prism optimizes expert placement and uses runtime migration for distributed MoE inference on heterogeneous edge GPUs, achieving up to 30.6% lower latency than baselines.
citing papers explorer
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ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving
ELDR reduces median TPOT by 5.9-13.9% in PD-disaggregated MoE serving via expert signatures from prefill, K-means partitioning, and locality-band routing with KV-co-indexed signature cache.
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ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving
ViBE co-optimizes expert placement with measured GPU performance variability in MoE inference to cut execution-time imbalance, delivering 14% better SLO attainment and up to 45% lower P90 TTFT.
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Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference
Task-aware expert grouping derived from family-specific co-activation traces cuts average communication cost 31.39% versus task-agnostic baselines in multi-task MoE inference while maintaining Jain fairness near 1.0.
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Birkhoff Decompositions and Photonic Interconnects Wait! Don't Forget the Compute!
A greedy max-weight decomposition strategy for MoE all-to-all communication on photonic fabrics improves overlap efficiency and reduces compute overheads compared to BvN by bounding the number of matchings.
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NanoCP: Request-Level Dynamic Context Parallelism for Data-Expert Parallel Decoding
NanoCP introduces request-level dynamic context parallelism to decouple MoE communication from KV cache placement in hybrid data-expert parallel serving, reporting up to 3.27x higher request rates and 2.12x lower P99 latency under TPOT SLOs.
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Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory
Expert-parallel scaling leaves per-expert routing imbalance flat; mock-token benchmarks overestimate real-text imbalance and fake a batch-size trend; architectures split into data-resilient (MHA, Mamba-2) and persistently concentrated (MLA, GDN) classes.
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GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems
GEM is a GPU-variability-aware expert-to-GPU mapping framework for MoE inference that classifies experts as consistent or temporal and places them to equalize finish times across heterogeneous GPUs.
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Hierarchical Mixture-of-Experts with Two-Stage Optimization
Hi-MoE uses two-level hierarchical routing objectives to enforce group-level balance while promoting within-group specialization, yielding better perplexity and expert utilization than prior MoE baselines in NLP and vision tasks.
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Federation of Experts: Communication Efficient Distributed Inference for Large Language Models
FoE restructures MoE blocks into per-KV-head clusters with sum-based synchronization, removing all-to-all communication in single-node settings and limiting it to intra-node in multi-node settings for up to 5.2x faster inference with comparable quality.
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SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
SpaceMoE partitions MoE layers across orbiting satellite subnets in a ring and optimizes expert placement by activation probability and path latency, yielding at least 3x lower inference latency in thousand-satellite simulations versus random baselines.
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Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns
Profiling shows persistent expert load imbalance and domain-specific activation patterns in large MoE models; workload-aware grouping and placement reduce all-to-all communication volume by up to 20x.
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Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.
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GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
GRACE-MoE integrates expert grouping, dynamic replication, and locality-aware routing with hierarchical sparse communication to reduce end-to-end latency in distributed SMoE inference.
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Replication in Graph Partitioning and Scheduling Problems
Replication reduces costs by 17-65% on average in hypergraph partitioning and 11-23% in DAG scheduling, sometimes eliminating communication needs entirely.
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Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Prism optimizes expert placement and uses runtime migration for distributed MoE inference on heterogeneous edge GPUs, achieving up to 30.6% lower latency than baselines.