DeepSpeed

DeepSpeed is an open-source deep learning optimization library with 43,000+ stars on GitHub that makes distributed training and inference of large models easy, efficient and effective.

Overview

Training a model with tens or hundreds of billions of parameters does not fit on a single GPU, and naive data parallelism wastes most of the memory on duplicated optimizer states and gradients. DeepSpeed attacks that waste with its ZeRO (Zero Redundancy Optimizer) family, which partitions optimizer states, gradients and parameters across GPUs so each device holds only a slice, and extends memory further with ZeRO-Offload and ZeRO-Infinity, which spill to CPU and NVMe storage. Layered on top are 3D parallelism (data, tensor and pipeline), mixture-of-experts support, model compression and a dedicated inference engine. DeepSpeed has been used to train landmark open models such as BLOOM, and the project — now stewarded by the DeepSpeedAI organization — remains actively developed, with releases and research updates continuing through October 2026.

Key Features

• ZeRO optimizer family – partitions optimizer states, gradients and parameters across devices to eliminate memory duplication.
• ZeRO-Offload and Infinity – offload memory to CPU and NVMe so very large models train on limited GPU fleets.
• 3D parallelism – combine data, tensor and pipeline parallelism, plus sequence parallelism for long contexts.
• Mixture-of-experts support – efficient training and inference for MoE architectures.
• Model compression – quantization, pruning and distillation tooling to shrink models for deployment.
• DeepSpeed Inference – optimized serving kernels for large transformer models.
• PyTorch-native – integrates with plain PyTorch code and the Hugging Face ecosystem.

💳 Pricing

DeepSpeed is completely free and open source under the Apache License 2.0. You run it on your own GPU clusters or cloud instances, so the only cost is the hardware itself.

Who Is It For

ML engineers and research teams training or serving large neural networks across multiple GPUs, who need to fit bigger models, train them faster and spend less on memory and interconnect.