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      "statement": "# ZeRO-Infinity: NVMe offload and memory-centric tiling\n\nExtended ZeRO with hierarchical GPU/CPU/NVMe offloading, memory-centric layer tiling, bandwidth-centric state partitioning and transfer/computation overlap.\n\n## Scientific scope\n\nExtends ZeRO and ZeRO-Offload rather than inventing sharded states or heterogeneous memory. Ability to fit or run a giant model is not completed high-quality end-to-end pretraining at that scale; throughput and overlap depend on hardware, batch sizes and bandwidth. Disk/CPU resources and transfer costs remain.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nSamyam Rajbhandari; Olatunji Ruwase; Jeff Rasley; Shaden Smith; Yuxiong He\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857v1)\n",
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