Overview
DeepSpeed is an open-source optimization library from Microsoft that focuses on distributed training, memory and inference efficiency (e.g., ZeRO, DeepSpeed-Inference, compression techniques). It enables researchers and engineers to train models from billions to trillions of parameters at much lower cost.
Key features
- ZeRO family memory optimizations and parallelism strategies for massive model training.
- High-performance inference and compression tools (ZeroQuant, XTC) to reduce latency and model size.
- Wide hardware and framework integrations (PyTorch, Azure, NVIDIA/AMD/Huawei support).
Use cases
- Training very large models on limited GPU resources to reduce infrastructure costs.
- Large-scale distributed training and inference, such as LLM training, inference services, and research reproduction.
- Deployments that require model compression and latency reduction for low-latency inference.
Technical highlights
- Combines system-level and algorithmic optimizations (parallel strategies, communication compression, heterogeneous memory management).
- Modular design to compose training, inference, and compression features for diverse workflows.
- Active community with extensive papers and tutorials, suitable for both research and production adoption.