ml-cost-optimization

Optimize and estimate ML infrastructure costs. Covers GPU selection and pricing (T4 through H200), training cost estimation and reduction, inference cost optimization, spot instance strategies, model compression (quantization, pruning, knowledge distillation), mixed precision, gradient accumulation, resource right-sizing, auto-scaling, scale-to-zero, batch inference, ONNX Runtime, storage tiering, cost tracking, FinOps for ML, LLM API cost sizing, and cloud vs on-prem comparison. Use when estimating or reducing ML costs, choosing a GPU or instance type, sizing a training/inference/LLM workload budget, analyzing experiment spend, or answering "how much will this model cost to train or serve".

Version

1.0

License

Apache-2.0