Which cloud platforms does this skill support?

The cost calculator currently supports pricing and performance benchmarks for Modal, Lambda Labs, and RunPod.

Does it account for LoRA or PEFT fine-tuning?

Yes, the skill includes parameters to adjust throughput and memory requirements for PEFT and LoRA, which typically reduce costs by 50-80%.

Can I use this for production inference planning?

Absolutely. It includes a specific tool for estimating monthly inference costs based on request volume, latency, and deployment type (serverless vs. dedicated).

How does it help in choosing a platform?

It runs a comparison script that evaluates your specific workload against current market rates to recommend the provider with the lowest total cost of ownership.

ML Training Cost Calculator

Name: ML Training Cost Calculator
Author: vanman2024

byvanman2024

Ciencia de Datos y ML

Calculates and compares machine learning training and inference costs across major cloud GPU platforms like Modal, Lambda Labs, and RunPod.

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This skill provides a comprehensive suite of tools for budgeting and optimizing ML projects by estimating GPU hours, training expenses, and production inference costs. It enables developers to compare pricing across serverless and dedicated platforms, evaluate the financial impact of optimization techniques like PEFT/LoRA, and generate structured cost breakdowns. Whether you are planning a small fine-tuning job or a large-scale production deployment, this tool ensures resource allocation is predictable and cost-effective.

Características Principales

Cross-platform pricing comparisons for Modal, Lambda Labs, and RunPod
GPU hour calculations based on model parameters and dataset size
Serverless vs. dedicated inference cost-benefit analysis
Automated cost-reduction modeling for PEFT, LoRA, and mixed precision
Structured cost breakdown templates for project budgeting
0 GitHub stars

Casos de Uso

Selecting the most cost-effective GPU and platform for fine-tuning LLMs
Estimating monthly inference expenses for production model scaling
Identifying the break-even point between serverless and dedicated GPU instances

Acerca de

Características Principales

Cross-platform pricing comparisons for Modal, Lambda Labs, and RunPod
GPU hour calculations based on model parameters and dataset size
Serverless vs. dedicated inference cost-benefit analysis
Automated cost-reduction modeling for PEFT, LoRA, and mixed precision
Structured cost breakdown templates for project budgeting
0 GitHub stars

Casos de Uso

Selecting the most cost-effective GPU and platform for fine-tuning LLMs
Estimating monthly inference expenses for production model scaling
Identifying the break-even point between serverless and dedicated GPU instances