Distributed Training Techniques for Large Language Models
Comprehensive survey of distributed LLM training approaches including data parallelism, tensor parallelism, pipeline parallelism, and ZeRO optimization.
2026 update note
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Editorial note for 2026. This does not replace the historical article below.
- Prefer current official docs for frameworks, APIs, and package names; sample code here is mostly pedagogical—check release notes when migrating.
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Recent picks: Observability · Graph RAG · Prompting & tools · Small-model deployment
Distributed Training for LLMs
Parallelism Strategies
Data Parallelism: Each GPU holds full model, averages gradients. FSDP shards optimizer states, gradients, and parameters.
Tensor Parallelism: Split layers across GPUs. Pipeline Parallelism: Partition layers into stages.
ZeRO-3: Shard everything, enabling 175B models with 512 GPUs at 10GB per GPU.
3D Parallelism: Pipeline between nodes, tensor within nodes, data for scaling to thousands of GPUs.
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