Fine-Tuning VRAM Calculator & ZeRO

The Fine-Tuning VRAM Breakdown Calculator & DeepSpeed ZeRO Scaling Matrix provides the exact mathematical formulas required to budget GPU cluster memory and prevent runtime CUDA Out-Of-Memory crashes; total training memory decomposes into 5 components: $M_{\text{total}} = M_{\text{weights}} + M_{\text{gradients}} + M_{\text{optimizer}} + M_{\text{activations}} + M_{\text{temp}}$; for full-parameter AdamW mixed-precision fine-tuning (parameter count $\Phi$): model weights require $2\Phi$ bytes (FP16), gradients $2\Phi$ bytes, and AdamW optimizer states require $12\Phi$ bytes (FP32 master weights $4\Phi$ + momentum $4\Phi$ + variance $4\Phi$), totaling a staggering $16\Phi$ bytes of static memory alone (70B model requires 1,120GB static VRAM); DeepSpeed ZeRO stages partition optimizer states (ZeRO-1), gradients (ZeRO-2), and parameters (ZeRO-3), slashing per-GPU memory to $\frac{16\Phi}{N}$ bytes alongside Activation Checkpointing.

GPU cluster capacity planning, DeepSpeed/FSDP configuration tuning, and diagnosing OOM bottlenecks.

Eliminates trial-and-error OOM crashes by providing exact mathematical formulas to configure ZeRO stages, batch sizes, and activation checkpointing parameters.

Derive the exact $16\Phi$ bytes static memory formula for full-parameter AdamW mixed-precision training breakdown?

Diagram ZeRO-1, ZeRO-2, and ZeRO-3 parameter/gradient/optimizer state partitioning across GPUs and compare their communication overheads?

Why does long-context sequence length blow up activation memory quadratically, and how does Activation Checkpointing trade 20% compute for 70% memory savings?

Derive the memory footprint of LoRA fine-tuning showing why frozen base weights eliminate 99% of optimizer and gradient memory?

How does FlashAttention-2 reduce attention activation memory from $O(N^2)$ to $O(N)$ via SRAM tiling and online softmax?