DeepSpeed

LLMs

DeepSpeed is Microsoft's open-source deep learning optimization library focused on training and serving ChatGPT-scale models at dramatically lower cost. It provides distributed training, memory optimization and inference acceleration, and is free to use in research and production.

Human verified · 2026-07-21 · Submit a correction

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Editor's note

Best for AI research teams and ML engineers who need to train or fine-tune large models; not suitable for general users who just want to call a ready-made model and lack GPU clusters or deep learning engineering experience.

CategoryLLMs

Human verified2026-07-21

What is DeepSpeed

Key features of DeepSpeed

Training or fine-tuning ChatGPT-style large language models at lower hardware cost
Running distributed training of very large models on multi-GPU clusters with limited memory
Accelerating large-model inference to reduce the compute cost of online serving
Building a custom large-model training pipeline on top of the open-source codebase

Good for

Open-sourced by Microsoft, free to use, with an active community and mature ecosystem
Meaningfully lowers the hardware barrier and cost of large-model training
Covers the full large-model lifecycle: training, fine-tuning and inference acceleration

Watch out

Aimed at professional deep learning engineering, with a steep learning curve for newcomers
Requires your own GPU compute and the ability to operate distributed clusters
The documentation and configuration surface is huge, so tuning demands solid engineering experience

How to use DeepSpeed

Make sure you have Python, PyTorch and working NVIDIA GPUs
Install DeepSpeed via pip and check version compatibility with CUDA and PyTorch
Read the official docs and write a DeepSpeed configuration for your training script
Replace your single-node launcher with the deepspeed launch command
Watch memory usage and throughput in the logs, then tune parallelism and memory-optimization levels step by step

Who DeepSpeed is for

Difficulty: Advanced

Training or fine-tuning ChatGPT-style large language models at lower hardware cost
Running distributed training of very large models on multi-GPU clusters with limited memory
Accelerating large-model inference to reduce the compute cost of online serving
Building a custom large-model training pipeline on top of the open-source codebase

FAQ

Is DeepSpeed free?

Yes. DeepSpeed is open-sourced by Microsoft; the code is publicly available and can be used for research and commercial production under its open-source license.

What is DeepSpeed used for?

It is mainly used to train and fine-tune ChatGPT-scale models at lower cost and to accelerate large-model inference, covering the main stages of large-model engineering.

Can regular users chat with DeepSpeed directly?

No. DeepSpeed is a low-level training and inference optimization library for developers, not a chat product; end users should use applications built on top of such technology.