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veScale-FSDP: Flexible and High-Performance FSDP at Scale
arXiv – CS AI|Zezhou Wang, Youjie Li, Zhiqi Lin, Jiacheng Yang, Cong Xie, Guanyu Feng, Zheng Zhong, Ziyue Huang, Hongyu Zhu, Zhi Zhang, Yanghua Peng, Xin Liu||6 views
🤖AI Summary
Researchers introduce veScale-FSDP, a redesigned Fully Sharded Data Parallel system that overcomes limitations of current FSDP implementations used for training large-scale AI models. The new system features flexible RaggedShard format and structure-aware planning, achieving 5-66% higher throughput and 16-30% lower memory usage while supporting advanced training methods and scaling to tens of thousands of GPUs.
Key Takeaways
- →veScale-FSDP addresses critical limitations in current FSDP systems that struggle with structure-aware training methods and non-element-wise optimizers.
- →The system introduces RaggedShard, a flexible sharding format that enables efficient data placement for block-wise quantization and advanced optimizers.
- →Performance improvements include 5-66% higher throughput and 16-30% lower memory usage compared to existing FSDP systems.
- →veScale-FSDP enables efficient scaling to tens of thousands of GPUs, addressing current scaling limitations.
- →The system natively supports cutting-edge models like Gemini and Kimi K2 with their advanced optimizer requirements.
#vescale-fsdp#distributed-training#gpu-scaling#model-training#performance-optimization#memory-efficiency#large-scale-models#parallel-computing#ai-infrastructure#throughput-improvement
Read Original →via arXiv – CS AI
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