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Practical Guide to GPU HBM for Fine-Tuning Models in Cloud

🔍 Running into CUDA out-of-memory errors is a common blocker when fine-tuning models; High Bandwidth Memory (HBM) holds model weights, optimizer state, gradients, activations, and framework overhead. The article breaks down those consumers, provides a simple HBM sizing formula, and walks through a 4B-parameter bfloat16 example that illustrates why full fine-tuning can require tens of GBs. It then presents practical mitigations—PEFT with LoRA, quantization and QLoRA, FlashAttention, and multi‑GPU approaches including data/model parallelism and FSDP—plus a sizing guide (16–40+ GB) to help choose the right hardware.