DeepSpeed
Information
DeepSpeed is Microsoft’s framework for efficient distributed training of very large models. It is well known for its
ZeRO optimization stages (1 to 3), which distribute parameters, gradients, and optimizer state across multiple
GPUs so larger models can be trained more efficiently.
It is especially relevant for very large models, including 70B+ scale workloads that may need to offload part of the
memory demand to CPU or NVMe.
Common use cases
- large-scale distributed model training,
- multi-GPU and multi-node fine-tuning,
- memory optimization for very large models,
- and enterprise or research workloads that exceed the limits of simpler single-node training setups.
Practical note
DeepSpeed becomes especially important when model size and memory pressure are the main bottlenecks. It is less about
beginner simplicity and more about scale, efficiency, and systems-level control.