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Using ZeRO and FSDP to Scale LLM Training on Multiple GPUs
Watch: Multi GPU Fine tuning with DDP and FSDP by Trelis Research Scaling large language model (LLM) training is no longer optional-it’s a necessity. As models grow from hundreds of millions to hundreds of billions of parameters, the computational demands outpace the capabilities of single GPUs. For example, training a 70B-parameter model on a single GPU is impossible due to memory and compute limits. ZeRO (Zero Redundancy Optimizer) and FSDP (Fully Sharded Data Parallel) address this by distributing training across multiple GPUs, enabling teams to handle models that would otherwise be infeasible. As mentioned in the Introduction to ZeRO and FSDP section, these frameworks reduce memory overhead by sharding model components across devices, making large-scale training practical even with limited hardware. LLMs are expanding rapidly. Open-source models like LLaMA and Miqu have pushed parameter counts beyond 70B, while research suggests that model performance continues to improve with scale. However, larger models require exponentially more resources. A 70B model can consume over 1TB of memory during training-a single H100 GPU offers only 80GB. Without memory optimization , teams face two choices: shrink models to fit hardware or invest in expensive multi-GPU clusters. ZeRO and FSDP eliminate this trade-off by sharding model parameters, gradients, and optimizer states across GPUs. This reduces memory usage per device, allowing you to train massive models on standard hardware setups.
