// multi-utility computation suite · offline · instant · precise
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│ computation suite │
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dev.training-compute-estimate Calculator
Calculates machine learning training compute requirements (FLOPS, GPU hours) from model size, dataset size, and batch size. Training compute scales as tokens × parameters × 6 FLOPs — the Chinchilla scaling law suggests optimal model size is proportional to training compute budget.
Inputs
Parameters B
Reference formula or conversion factor shown for context.
Training Tokens B
Reference formula or conversion factor shown for context.
Gpu Flops Tflops
Reference formula or conversion factor shown for context.
Gpu Utilization Pct
Reference formula or conversion factor shown for context.
Results
total FLOPs (Chinchilla law: 6ND)
The combined total across all inputs and components.
GPU-hours needed
The result expressed in hours. Divide by 24 to convert to days.
GPU-days
Reference formula or conversion factor shown for context.
utilization
Fraction of capacity actually being used. Above 80–85% is typically considered high utilisation; above 95% leads to queuing and latency spikes.