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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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MiniMax Sparse Attention

Xunhao Lai, Weiqi Xu, Yufeng Yang, Qiaorui Chen 2026-06-14
Sparsity

The problem is that quadratic-cost softmax attention makes ultra-long-context LLM inference untenable at deployment scale. The method, MiniMax Sparse Attention (MSA), uses a lightweight Index Branch for blockwise Top-k selection per GQA group and a Main Branch for exact block-sparse attention, co-designed with an exp-free GPU kernel. On a 109B multimodal model, MSA reduces per-token attention compute by 28.4x at 1M context and achieves 14.2x prefill and 7.6x decoding speedups on H800. This matters because it enables practical deployment of frontier LLMs with million-token contexts for agentic workflows and repository-scale reasoning.

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