Distributed Training Techniques for Large Language Models
Comprehensive survey of distributed LLM training approaches including data parallelism, tensor parallelism, pipeline parallelism, and ZeRO optimization.
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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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Cite as: “Distributed Training Techniques for Large Language Models,” AI Dev Hub, https://aidevhub.net/blog/distributed-llm-training
Markdown for AI/LLM consumption: /blog/distributed-llm-training.md · full corpus: /llms-full.txt
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