【Application Scenarios】

• Work scenarios: Training and inference of large-scale deep learning models
• Life scenarios: Not applicable

【Target Users】

• Researchers and engineers
• Teams that need to train or infer large-scale deep learning models

【Core Features】

• Provides a deep learning optimization library, making distributed training simple, efficient, and effective
• Supports training/inference of dense or sparse models, with parameter scales ranging from tens of billions to trillions
• Achieves superior system throughput and efficiently scales to thousands of GPUs
• Conducts training/inference on GPU systems with limited resources
• Realizes unprecedented low latency and high throughput inference
• Achieves extreme compression at low cost, reducing inference latency and model size

【Is It Free】

• Yes, DeepSpeed is an open-source project

【Community Ecosystem】

• DeepSpeed has been widely adopted, supporting various popular open-source deep learning frameworks
• Active community contributions, with detailed contribution guidelines and code of conduct

【Summary】

• DeepSpeed is a powerful deep learning optimization software suite, aimed at making large-scale deep learning training and inference simple, efficient, and effective through system innovations. It supports model training and inference with parameters ranging from tens of billions to trillions, offering various innovative technologies including ZeRO, 3D parallelism, DeepSpeed-MoE, greatly improving the efficiency and usability of large-scale model training.