{"ID":2842448,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10753","arxiv_id":"2511.10753","title":"FengHuang: Next-Generation Memory Orchestration for AI Inferencing","abstract":"This document presents a vision for a novel AI infrastructure design that has been initially validated through inference simulations on state-of-the-art large language models. Advancements in deep learning and specialized hardware have driven the rapid growth of large language models (LLMs) and generative AI systems. However, traditional GPU-centric architectures face scalability challenges for inference workloads due to limitations in memory capacity, bandwidth, and interconnect scaling. To address these issues, the FengHuang Platform, a disaggregated AI infrastructure platform, is proposed to overcome memory and communication scaling limits for AI inference. FengHuang features a multi-tier shared-memory architecture combining high-speed local memory with centralized disaggregated remote memory, enhanced by active tensor paging and near-memory compute for tensor operations. Simulations demonstrate that FengHuang achieves up to 93% local memory capacity reduction, 50% GPU compute savings, and 16x to 70x faster inter-GPU communication compared to conventional GPU scaling. Across workloads such as GPT-3, Grok-1, and QWEN3-235B, FengHuang enables up to 50% GPU reductions while maintaining end-user performance, offering a scalable, flexible, and cost-effective solution for AI inference infrastructure. FengHuang provides an optimal balance as a rack-level AI infrastructure scale-up solution. Its open, heterogeneous design eliminates vendor lock-in and enhances supply chain flexibility, enabling significant infrastructure and power cost reductions.","short_abstract":"This document presents a vision for a novel AI infrastructure design that has been initially validated through inference simulations on state-of-the-art large language models. Advancements in deep learning and specialized hardware have driven the rapid growth of large language models (LLMs) and generative AI systems. H...","url_abs":"https://arxiv.org/abs/2511.10753","url_pdf":"https://arxiv.org/pdf/2511.10753v1","authors":"[\"Jiamin Li\",\"Lei Qu\",\"Tao Zhang\",\"Grigory Chirkov\",\"Shuotao Xu\",\"Peng Cheng\",\"Lidong Zhou\"]","published":"2025-11-13T19:11:39Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}