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ITME: Inference Tiered Memory Expansion with Disaggregated CXL-Hybrid Memories

Hakbeom Jang, Younghoon Min, Sunwoong Kim, Taeyoung Ahn 2026-06-14
Inference

ITME addresses the problem of scaling shared context infrastructure for TB-scale LLM inference workloads beyond individual server capacity. The method leverages CXL-hybrid memory to provide massive, byte-addressable remote memory expansion, simplifying the software stack by eliminating complex software-level optimization. Experimental evidence from production-grade SK Hynix CMM and PCIe Gen5 NVMe SSDs, along with an FPGA prototype, shows up to a 35.7% throughput improvement over conventional CPU-offloading. This matters because ITME enables cost-efficient scaling of shared context layers for agentic and long-context LLMs by proactively managing data movement across the memory-storage hierarchy.

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Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training

Yuming Zhou, Haoyang Li, Sheng Lin, Yanfeng Zhao 2026-06-14
Language Model LLM Training GPU Hardware ×

ForeMoE addresses expert load imbalance in Mixture-of-Experts (MoE) models during reinforcement learning (RL) post-training, where existing step-level statistics fail due to high-frequency micro-step fluctuations. The method exploits foreseeable routing information from the rollout stage to proactively guide load balancing, using a hierarchical planner to decompose the NP-hard problem and a transfer engine for overlapped expert transfer. Evaluations on 64 GPUs show up to a 1.45× speedup over state-of-the-art RL post-training systems. This matters because it enables efficient scaling of MoE LLMs under the unique workload dynamics of RL post-training, a dominant paradigm in current LLM development.

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Characterizing Software Aging in GPU-Based LLM Serving Systems

Domenico Cotroneo, Bojan Cukic 2026-06-14
GPU LLM Serving

The paper addresses the problem of software aging in GPU-based LLM serving systems, which differ from traditional CPU-centric systems due to heterogeneous hardware and highly variable workloads. The method involves a 216-hour empirical campaign across six co-located deployments with identical stress, monitoring host, device, and client metrics and applying a statistical pipeline for autocorrelation and multiple testing. Experimental evidence shows statistically significant memory aging in all deployments, with leak rates strongly dependent on the serving runtime and configuration. This matters because it provides a reproducible framework bridging software aging and rejuvenation research with LLM serving, enabling future mitigation strategies.

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