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Awesome GitHub RepositoriesMemory Optimization Techniques
Methods and strategies for reducing memory footprint during large-scale model training and inference.
Distinguishing note: Focuses specifically on memory management and offloading strategies for AI training, distinct from general-purpose database or system memory management.
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Awesome Memory Optimization Techniques GitHub Repositories
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- deepspeedai/DeepSpeed
deepspeedai/DeepSpeed
41,638View on GitHubDeepSpeed is a high-performance library designed to scale deep learning model training and inference across massive clusters of GPUs and compute nodes. It provides a comprehensive suite of tools for distributed training, enabling the execution of models that exceed the memory capacity of single devices through advanced parameter partitioning, pipeline-based model parallelism, and memory-efficient state offloading. The framework distinguishes itself through specialized communication-efficient optimizers and hardware-aware acceleration techniques. By utilizing gradient compression, quantization
Reduces GPU memory consumption during large-scale training by offloading optimizer states to the host CPU.
Pythonbillion-parameterscompressiondata-parallelism
