DeepSpeed

Its ZeRO (Zero Redundancy Optimizer) technology partitions model states (parameters, gradients, optimizer states) across GPUs and nodes rather than replicating them on each device, dramatically reducing per-GPU memory requirements and enabling models that would otherwise require prohibitively large GPU clusters.

Beyond ZeRO, DeepSpeed provides a suite of optimizations: mixed-precision training with loss scaling, activation checkpointing to trade compute for memory, gradient compression for faster distributed communication, and offloading of optimizer states to CPU or NVMe storage.

The library integrates with Hugging Face Transformers and other PyTorch frameworks through a simple configuration file, allowing existing training code to benefit from DeepSpeed's optimizations without significant refactoring.

DeepSpeed Inference adds optimizations for serving large models including kernel fusion and quantization.

Research labs and AI companies training large language models and vision-language models at 7B to 100B+ parameter scales use DeepSpeed as the training infrastructure layer that makes those runs feasible on available hardware budgets.

The library has been used in training runs for models including BLOOM, Megatron-DeepSpeed, and various open-source LLMs from the research community.

Its Hugging Face integration makes its benefits accessible to practitioners training smaller models as welleven 7B parameter models benefit meaningfully from ZeRO-3's memory efficiency on standard 8-GPU server configurations.