What is ML Capacity Planning?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

Build forecasts from three inputs: historical usage trends (GPU hours consumed monthly over the past 6-12 months, plotted with growth rate), planned model deployments (each new production model typically requires 2-4 GPU instances for serving plus training compute), and business growth projections (prediction volume scales with user/transaction growth). Apply a 1.5x buffer for training (experiments fail and retry) and 2x buffer for serving (handle traffic spikes without degradation). Model capacity in three tiers: baseline (minimum always-on), elastic (auto-scaled for peak periods), and burst (spot instances for training jobs). Review forecasts quarterly against actual usage and adjust. Tools like Kubecost or CloudHealth track current utilization and project future needs. For most growing companies, plan for 50-100% annual compute growth.

Apply five optimization strategies: right-size GPU instances by profiling actual utilization (most teams over-provision by 40-60%), implement spot/preemptible instances for training workloads (60-90% cost reduction with checkpoint-based fault tolerance), use autoscaling for inference endpoints based on actual traffic patterns rather than peak provisioning (reduces serving costs 30-50%), schedule non-urgent training jobs during off-peak hours when spot instance prices are lowest, and consolidate underutilized inference endpoints using multi-model serving (NVIDIA Triton supports serving multiple models on shared GPU resources). Track cost-per-prediction as a key metric and review monthly. Set budget alerts at 80% of monthly allocation to prevent surprise overruns. Most organizations achieve 30-50% cost reduction without performance impact through these optimizations.

Build forecasts from three inputs: historical usage trends (GPU hours consumed monthly over the past 6-12 months, plotted with growth rate), planned model deployments (each new production model typically requires 2-4 GPU instances for serving plus training compute), and business growth projections (prediction volume scales with user/transaction growth). Apply a 1.5x buffer for training (experiments fail and retry) and 2x buffer for serving (handle traffic spikes without degradation). Model capacity in three tiers: baseline (minimum always-on), elastic (auto-scaled for peak periods), and burst (spot instances for training jobs). Review forecasts quarterly against actual usage and adjust. Tools like Kubecost or CloudHealth track current utilization and project future needs. For most growing companies, plan for 50-100% annual compute growth.

Apply five optimization strategies: right-size GPU instances by profiling actual utilization (most teams over-provision by 40-60%), implement spot/preemptible instances for training workloads (60-90% cost reduction with checkpoint-based fault tolerance), use autoscaling for inference endpoints based on actual traffic patterns rather than peak provisioning (reduces serving costs 30-50%), schedule non-urgent training jobs during off-peak hours when spot instance prices are lowest, and consolidate underutilized inference endpoints using multi-model serving (NVIDIA Triton supports serving multiple models on shared GPU resources). Track cost-per-prediction as a key metric and review monthly. Set budget alerts at 80% of monthly allocation to prevent surprise overruns. Most organizations achieve 30-50% cost reduction without performance impact through these optimizations.

Build forecasts from three inputs: historical usage trends (GPU hours consumed monthly over the past 6-12 months, plotted with growth rate), planned model deployments (each new production model typically requires 2-4 GPU instances for serving plus training compute), and business growth projections (prediction volume scales with user/transaction growth). Apply a 1.5x buffer for training (experiments fail and retry) and 2x buffer for serving (handle traffic spikes without degradation). Model capacity in three tiers: baseline (minimum always-on), elastic (auto-scaled for peak periods), and burst (spot instances for training jobs). Review forecasts quarterly against actual usage and adjust. Tools like Kubecost or CloudHealth track current utilization and project future needs. For most growing companies, plan for 50-100% annual compute growth.

Apply five optimization strategies: right-size GPU instances by profiling actual utilization (most teams over-provision by 40-60%), implement spot/preemptible instances for training workloads (60-90% cost reduction with checkpoint-based fault tolerance), use autoscaling for inference endpoints based on actual traffic patterns rather than peak provisioning (reduces serving costs 30-50%), schedule non-urgent training jobs during off-peak hours when spot instance prices are lowest, and consolidate underutilized inference endpoints using multi-model serving (NVIDIA Triton supports serving multiple models on shared GPU resources). Track cost-per-prediction as a key metric and review monthly. Set budget alerts at 80% of monthly allocation to prevent surprise overruns. Most organizations achieve 30-50% cost reduction without performance impact through these optimizations.