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ZeRO-Infinity: NVMe offload and memory-centric tiling

Contribution grade

Heterogeneous large-model training systems · Human-designed research method or resource

Reference score130,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Extended ZeRO with hierarchical GPU/CPU/NVMe offloading, memory-centric layer tiling, bandwidth-centric state partitioning and transfer/computation overlap.

Scientific scope

Extended ZeRO with hierarchical GPU/CPU/NVMe offloading, memory-centric layer tiling, bandwidth-centric state partitioning and transfer/computation overlap. Extends 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.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Extends 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.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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