{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/distributed-training-systems",
  "headline": "Distributed Training: FSDP, DeepSpeed, and Scaling Laws",
  "description": "Training frontier AI models requires thousands of GPUs working in parallel. FSDP and DeepSpeed ZeRO are the dominant strategies for memory-efficient distributed training, enabling models with hundreds of billions of parameters.",
  "dateCreated": "2026-05-24T02:49:13.601Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models",
      "sameAs": "https://arxiv.org/abs/1910.02054"
    },
    {
      "@type": "CreativeWork",
      "name": "PyTorch Fully Sharded Data Parallel (FSDP)",
      "sameAs": "https://pytorch.org/docs/stable/fsdp.html"
    }
  ]
}