GPU memory math for full-parameter fine-tuning: sizing VRAM before you rent

Full-parameter fine-tuning requires far more GPU memory than inference because it stores BF16 weights, gradients, FP32 master weights, and optimizer moments, typically totaling about 16 bytes per parameter with AdamW, or roughly 112 GB for a 7B model before activations and runtime overhead. The discussion provides equations for deriving a decoder-only transformer’s true parameter count from its configuration, estimating resident model state and activation memory, and selecting the lowest DeepSpeed ZeRO or FSDP sharding stage that fits while minimizing communication overhead. It emphasizes using FlashAttention and gradient checkpointing to reduce activation memory, accurately accounting for decimal GB versus GPU GiB capacity, maintaining a 2–4 GB overhead margin, and using gradient accumulation rather than increasing micro-batch size. For a Llama 3 8B model at 4K context with full checkpointing, two 80 GB A100 GPUs using ZeRO-1 can fit narrowly, while ZeRO-2 provides more safety and a single H200 can avoid distributed-training complexity. The text also compares estimated cost and speed tradeoffs among A100, H100, and H200 configurations, recommends measuring actual allocator behavior and optimizer state on target hardware, and covers practical concerns including checkpoint storage, interruption-tolerant capacity, NCCL setup, and monitoring VRAM use during long runs.

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