Infrastructure Guide
AI Capacity Planning Guide (2026) - Scale & Resource Forecasting
AI capacity planning: forecast traffic (3-6 month projections), size resources (API quotas, self-hosted GPU), scaling strategy (horizontal vs vertical), cost projections. Buffer: 2x current peak capacity. Monitoring: track utilization, alert at 80% capacity. Review: quarterly capacity assessment.
Direct answer
AI capacity planning: forecast traffic (3-6 month projections), size resources (API quotas, self-hosted GPU), scaling strategy (horizontal vs vertical), cost projections. Buffer: 2x current peak capacity. Monitoring: track utilization, alert at 80% capacity. Review: quarterly capacity assessment.
Fast path
- Forecast: project traffic 3-6 months based on adoption trends.
- Resources: size API quotas, GPU capacity for self-hosted.
- Buffer: maintain 2x current peak capacity for unexpected spikes.
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Implementation Steps
- Forecast: project traffic 3-6 months based on adoption trends.
- Resources: size API quotas, GPU capacity for self-hosted.
- Buffer: maintain 2x current peak capacity for unexpected spikes.
- Scaling: plan horizontal (more instances) vs vertical (larger models).
- Review: quarterly assessment, adjust projections based on actuals.
Frequently Asked Questions
How to plan AI capacity?
Plan AI capacity: forecast traffic 3-6 months, size resources (API quotas, GPU), maintain 2x peak buffer, plan scaling (horizontal instances or vertical model size). Monitor utilization, alert at 80% capacity. Quarterly review to adjust projections.
How much GPU capacity for AI?
GPU capacity for AI: estimate concurrent requests × tokens/sec × model size. Llama 7B: 1x A10G per 10 concurrent, Llama 70B: 4x A100 per 20 concurrent. Monitor GPU utilization, scale at 80%. Buffer 2x for peaks. Cost: $0.50-12/hr depending on model and scale.
Related Guides
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AI Infrastructure Planning Guide (2026) - Capacity and Cost Planning
Plan AI infrastructure: GPU requirements, API vs self-hosted, scaling costs, latency optimization. Make build vs buy decisions.
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Calculate AI data pipeline ROI: infrastructure costs, processing value, quality improvements. Business case for data investments.
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