Unverified60% confidenceFactExact time
DeepSpeed's ZeRO optimizer can reduce the GPU memory required per device to 1/N of the original by sharding optimizer states, gradients, and model parameters.
2
Sources
60%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
分布式AI系统：训练、推理与生产部署工程实践指南
Related Claims
Unverified微软DeepSpeed提出的ZeRO技术分为三个阶段：Stage-1分片优化器状态可将内存需求降低约4倍，Stage-2额外分片梯度，Stage-3进一步分片模型参数73% similarUnverifiedZeRO was proposed by the DeepSpeed team in 2020 and eliminates redundancy in data parallelism through three stages: ZeRO-1 shards optimizer states, ZeRO-2 shards gradients, and ZeRO-3 shards model parameters.72% similarUnverifiedZeRO-Infinity可将分片数据溢出至CPU内存乃至NVMe SSD69% similarUnverifiedNVIDIA的Megatron-LM、Microsoft的DeepSpeed为大规模分布式训练提供基础设施，ZeRO用于降低GPU显存占用68% similarUnverifiedNVIDIA recommends a top-down capacity planning approach: define SLOs, run benchmark tests, and calculate the total number of GPUs needed.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/502864API
curl https://kongchang.com/api/v1/knowledge/claims/502864MCP
get_claim(id=502864)