Local Minimax M2 7 GTA benchmark

Your team just spent another hour debating whether to build with cloud AI APIs or run models locally. The decision keeps getting pushed back. Engineers want to experiment but don't know if the infrastructure can handle it. Meanwhile, you're burning budget on API calls that add up. Sound familiar? Every week without a decision costs you in wasted meetings, missed deadlines, and uncertainty about your architecture.

This skill cuts through the confusion in minutes. It walks through what you actually need — speed, cost, privacy, capacity — and gives you a clear recommendation with reasons you can actually explain to stakeholders. No whiteboard sessions. No jargon. Just a decision you can move forward with.

Whether you pick cloud APIs or local models, you get a working setup you can actually run. No more hypotheticals. No more waiting for someone to make a call.

What you get:

• decision_matrix.yaml — Weighted scoring system comparing cost, latency, privacy, and scalability across your options
• cost_calculator.xlsx — Spreadsheet that reveals hidden API fees and local infrastructure investments over 12 months
• privacy_checklist.md — Compliance requirements checklist for handling data locally versus sending it to cloud services
• infrastructure_guide.md — Step-by-step instructions for setting up local inference on your existing laptop hardware
• stakeholder_brief.docx — One-page summary with pros, cons, and total cost of ownership to present to decision-makers
• team_handoff.md — Documentation template so engineers know exactly what tool handles what task
• troubleshooting_faq.md — Common mistakes and fixes for both cloud and local deployment paths
• vendor_comparison.md — Quick-reference chart comparing top cloud API providers and open-source models by use case