DeepSpeed

Overview

DeepSpeed is Microsoft's open source library for making large model training and inference cheaper on the hardware a team already has. Its ZeRO memory optimisations split optimiser state, gradients and parameters across devices so models fit in less memory, and the same library covers pipeline and tensor parallelism, mixture-of-experts training and inference, long sequence attention kernels and data loading acceleration. A flops profiler and communication logging are built in, so the efficiency question, what the run actually costs in compute, can be measured rather than estimated. Getting-started material covers Azure and accelerator setup guides, and the project runs a public blog and tutorial series. It is free, Apache 2.0 licensed, and integrated into several training stacks.

Editorial verdict

The strongest case is arithmetic: if ZeRO or offload lets a run fit on fewer GPUs, the energy and cost saving follows directly, and the built-in flops profiler lets a team check the claim on its own workload instead of trusting a benchmark. It is a library, not a product, so the real cost is engineering time and the configuration work that comes with it, which is where teams stall. Coverage of training and inference in one project reduces stack sprawl. Pairs naturally with a measurement tool such as CodeCarbon if the objective is reporting as well as speed. Free under Apache 2.0, with the usual open source trade-off of community support.

Written by the GreenAIX editors from the recorded fields above. Vendors do not review or approve verdicts.

Use cases

• Train a model that does not fit on one GPU
• Reduce GPU count for a fixed training run
• Profile a run's compute and communication cost
• Serve large models with less memory

Sources

• DeepSpeed home