arXiv AI July 23, 2026 By Minyu Cui, Anna Wingkvist, Morgan Ericsson

Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts

arXiv:2607. 19539v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 15

mKernel: Fast Multi-GPU, Multi-Node Fused Kernels

arXiv:2609.13585v1 Announce Type: cross Abstract: Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granul...

By Ziming Mao, Yihan Zhang, Shawn Wei Chew, Shuang Ma, Costin Raiciu, Yang Zhou, Scott Shenker, Ion Stoica

arXiv AI

Jun 10

Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design

arXiv:2606. 10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads.

By Wenxin Wang, Yule Hou, Yu Ji, Peng Qu, Youhui Zhang

efficiency

arXiv Machine Learning

4d ago

Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging

arXiv:2609.38090v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models are a compelling architecture for scaling model capacity, making them especially attractive for deployment on resource-...

By Sanjali Yadav, Bahar Asgari

efficiency benchmarks

arXiv Machine Learning

2d ago

Efficient Expert-Parallel Communication on PCIe-Connected Consumer GPUs

arXiv:2609.40093v1 Announce Type: cross Abstract: Expert parallelism (EP) enables inference of large Mixture-of-Experts (MoE) models by placing their experts across multiple GPUs, but requires substa...

By Jaehwan Lee, Sangmin Lee, Chaewon Kim, Junsik Shin, Jaejin Lee

llms benchmarks

arXiv AI

Jun 9

Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

arXiv:2606. 09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems.

By Minyu Cui, Miquel Pericas

efficiency

arXiv Machine Learning

Jun 2

Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap

arXiv:2512. 10236v2 Announce Type: replace-cross Abstract: Modern ML workloads demand distributing training and inference across multiple GPUs.

By Shagnik Pal, Shaizeen Aga, Suchita Pati, Mahzabeen Islam, Lizy K. John

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mKernel: Fast Multi-GPU, Multi-Node Fused Kernels

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Mira: Memory-Efficient MoE Inference Using Adaptive Caching and Predictive Expert Staging

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Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap