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Runtime environments that enable partitioning of model states and activations across multiple hardware devices.
Distinguishing note: Focuses on memory management and state partitioning for large models rather than general distributed training orchestration.
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DeepSpeed 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
A runtime environment that partitions massive model states and activations across multiple hardware devices to overcome memory constraints.