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AI Meeting Assistant GPU Requirements Estimator

The AI Meeting Assistant GPU Requirements Estimator calculates the number of GPUs needed to sustain a peak meeting analysis workload. It combines peak task rate with measured GPU-seconds per task, adjusts for target utilization, and adds reserve headroom before rounding to whole devices.

The result supports initial capacity planning and comparison of model or hardware configurations. It is most reliable when GPU-seconds per task come from representative benchmarks that include the same model size, precision, batching, and input profile expected in production.

AI Meeting Assistant GPU sizing inputs

tasks/min
GPU-sec
%
%
GPUs
Result
—
GPUs required at peak load
Raw GPU demand—
Demand after utilization—
Equivalent server nodes—
Estimated utilization with rounded GPUs—

1. Step 1
Measure peak tasks per minute over the interval the system must sustain.

2. Step 2
Enter benchmarked GPU-seconds consumed by one representative task.

3. Step 3
Choose a target utilization below 100% to preserve scheduling and latency flexibility.

4. Step 4
Add reserve headroom for spikes, failures, and model variance.

5. Step 5
Enter GPUs per server node to translate device count into deployable nodes.

6. Step 6
Review both GPU and node counts, then validate the assumptions with a load test.

Raw GPU demand = peak tasks per minute ÷ 60 × GPU-seconds per task. Utilization-adjusted demand = raw GPU demand ÷ target utilization as a decimal. Required GPUs = ceiling(adjusted demand × (1 + headroom ÷ 100)). Nodes = ceiling(required GPUs ÷ GPUs per node).

What the result means

The main result is the whole-device count needed to meet the entered peak rate at the target utilization with reserve headroom.

Memory capacity, interconnect limits, CPU preprocessing, and model loading may require more GPUs than the compute-time estimate alone.

Given: 90 tasks per minute, 20 GPU-seconds per task, 70% target utilization, 20% headroom, and 8 GPUs per node.

Calculation: Raw demand = 90 ÷ 60 × 20 = 30.00 GPUs. Adjusted demand = 30.00 ÷ 0.70 = 42.86. With headroom: 42.86 × 1.20 = 51.43, rounded up.

Result: The workload requires 52 GPUs under these assumptions.

What is GPU-seconds per task?

It is the wall-clock seconds of one fully occupied GPU consumed by an average task. Sum equivalent usage when a task uses multiple GPUs.

Can I use vendor benchmark latency?

Only if its model, hardware, batch size, and input profile closely match production. Internal measurements are safer.

Why set utilization below 100%?

Operating at full theoretical utilization leaves little room for variability, scheduling gaps, or latency-sensitive work.

Does the estimate guarantee enough GPU memory?

No. Check model weights, activations, KV cache, batch size, and parallelism separately.

How do batching improvements affect the result?

Efficient batching generally reduces GPU-seconds per task. Re-measure that input after changing batch strategy.