arXiv AI June 15, 2026 By Anas Nassar, Steve Mohr, Leonard Apanasevich, Himanshu Sharma

STREAM: Multi-Tier LLM Inference Middleware with Dual-Channel HPC Token Streaming

arXiv:2606. 13968v1 Announce Type: cross Abstract: Researchers and practitioners working with large language models face a fragmented landscape: local models are free and private but hardware limits the model size and context windows a researcher can use; institutional HPC centers offer powerful GPU resources at no marginal cost and keep data within institutional boundaries, but operate behind firewalls and are designed for batch jobs rather than interactive use; commercial cloud APIs provide frontier-model quality on demand but impose significant cost and data retention policies unsuitable for sensitive research data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 2

Lodestar: An Online-Learning LLM Inference Router

arXiv:2606. 00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for GPU utilization.

By Gangmuk Lim, Wanyu Zhao, Brighten Godfrey, Jiaxin Shan, Le Xu, Liguang Xie

llms benchmarks

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 AI

Jun 16

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.

By Yicheng Feng, Xin Tan, Yangtao Deng, Yimin Jiang, Yibo Zhu, Hong Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Aug 3

Topology-Aware Data Movement for Disaggregated GPU Inference

arXiv:2607. 28633v1 Announce Type: cross Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly.

By Sanjeev Rao Ganjihal

llms efficiency

arXiv Machine Learning

1d ago

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.

By Anders Vestrum, Arya Raeesi, Hanna Roed

llms

arXiv Machine Learning

Aug 11

MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs

arXiv:2510. 19366v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) scales model capacity through sparse activation, and is becoming an important architecture for large language models (LLMs).

By Xinfeng Xia, Xiaofeng Hou, Jiacheng Liu, Wenfeng Wang, Mingxuan Zhang, Peng Tang, Chao Li, Minyi Guo

llms efficiency

Related stories

Lodestar: An Online-Learning LLM Inference Router

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

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

Topology-Aware Data Movement for Disaggregated GPU Inference

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs