The AI Agent GPU Requirements Estimator approximates accelerator demand from task traffic, model-call frequency, token generation, and measured GPU throughput. It converts the workload into required GPU count and adds a utilization target plus failover reserve instead of assuming every device can run at 100%.
This is intended for capacity planning when a model is self-hosted or served through dedicated GPU instances. It can help compare model sizes, batching strategies, and traffic forecasts, but it should be calibrated with benchmark data from the exact model, precision, context length, and serving stack.
1. Enter traffic
Use the expected average number of completed agent tasks per minute.
2. Describe model demand
Enter model calls per task and generated tokens per call.
3. Use benchmark throughput
Provide effective tokens per second from a representative serving benchmark.
4. Set operating headroom
Choose target utilization below 100% and add reserve for spikes or failures.
5. Review whole-GPU demand
Use the rounded count for planning and the unrounded count for sensitivity analysis.
Planned GPUs is the whole-device count needed to meet the entered average generation load at the selected utilization and reserve levels.
Prefill cost, input context, memory limits, batching efficiency, quantization, and latency targets are not fully represented by output throughput alone.
Given: 120 tasks/minute, 2 model calls per task, 700 generated tokens per call, 250 tokens/second per GPU, 75% utilization, and 20% reserve.
Calculation: Required rate = 120 × 2 × 700 ÷ 60 = 2,800 tokens/second. Usable GPU rate = 250 × 0.75 = 187.5 tokens/second. Base GPUs = 2,800 ÷ 187.5 = 14.93. With reserve: 14.93 × 1.20 = 17.92.
Result: Round up to 18 GPUs.
No. It estimates throughput capacity. Confirm that model weights, KV cache, and batch sizes fit in memory separately.
This simplified model focuses on generation throughput. For prefill-heavy workloads, use a benchmark that already reflects the input/output mix.
Sustained operation near 100% leaves little room for bursts, variance, or maintenance.
Possibly, if memory and compute allow it. Enter aggregate measured throughput per physical GPU.
It is a planning estimate, not a substitute for load testing on the target hardware and serving software.