// multi-utility computation suite · offline · instant · precise
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dev.neural-network-memory Calculator
Calculates neural network training memory requirements from model parameter count, batch size, and precision from GPU VRAM sizing. Memory usage = parameters × 4 bytes (FP32) × (weights + gradients + optimizer state) — Adam optimizer triples the memory of the model weights alone.
Inputs
Parameters B
Reference formula or conversion factor shown for context.
Precision
Reference formula or conversion factor shown for context.
Batch Size
Reference formula or conversion factor shown for context.
Sequence Length
Linear measurement. Ensure consistent units: 1 m = 1,000 mm = 3.281 ft.
Results
parameter memory (GB)
Reference formula or conversion factor shown for context.
KV cache estimate (GB)
CAC (customer acquisition cost) -- total sales and marketing spend divided by new customers acquired. Healthy LTV:CAC ratio is 3:1 or higher.
total VRAM needed (GB)
The combined total across all inputs and components.
precision
Sample size or count used in the calculation.
rule of thumb
A widely used practical approximation or heuristic for this field — useful for quick estimates but not a substitute for rigorous calculation.