deepspeed

To configure ZeRO optimization for distributed training, you apply specific ZeRO stages (1-3) to partition optimizer states, gradients, and parameters, dramatically reducing the memory footprint across GPUs. The Skill provides actionable setup instructions and configuration templates to implement this memory optimization.

Pipeline parallelism splits model layers across multiple GPUs and nodes to scale training for extremely large AI models. You need it when a single GPU cannot hold the model, enabling 3D parallelism to overcome memory and compute barriers across large GPU clusters.

Yes, you can use mixed precision training with FP16, BF16, and FP8 in DeepSpeed to accelerate compute and reduce memory. The Skill offers expert guidance on configuring these precision techniques alongside 1-bit Adam for lower communication overhead.

To implement DeepSpeed-MoE and sparse attention for large models, you apply Mixture of Experts and sparse attention techniques to scale model parameters efficiently. The Skill provides real-world guidance and best practices for deploying these features.

Yes, DeepSpeed supports multi-GPU inference optimization for GPT and MT-NLG scale models. The Skill provides actionable configuration templates and references to official documentation to optimize inference workloads across distributed GPU environments.