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AI Video Pipeline GPU Requirements Estimator

The AI Video Pipeline GPU Requirements Estimator calculates approximate GPU count from video volume, processing time per video, batch concurrency per GPU, completion window, utilization, and redundancy. It is intended for batch inference or multimodal enrichment workloads where GPU time is the main constrained resource.

The estimate helps teams compare hardware profiles and determine whether a target service window is feasible. Real demand depends on codec, resolution, frame sampling, model architecture, precision, memory, and data-transfer behavior, so benchmark time and concurrency should be measured on the intended stack.

Calculator inputs

videos
sec
videos
hours
%
%
Result
—
Estimated GPU count
Total GPU work—
Effective work per GPU—
Base GPU demand—
Reserve-adjusted demand—

1. Enter batch volume
Use the videos that must finish within one target processing window.

2. Measure GPU time per video
Use end-to-end GPU-active seconds from a representative benchmark.

3. Set per-GPU concurrency
Enter simultaneous videos sustained per GPU at acceptable memory use and latency.

4. Define the completion window
Enter available processing hours for the batch.

5. Choose utilization and reserve
Apply headroom for scheduling inefficiency, variance, and failures.

6. Review rounded GPU count
Round up and confirm storage and network can feed the calculated fleet.

Total GPU Work = Videos × GPU Seconds per Video
Effective Work per GPU = Window Seconds × Concurrent Videos per GPU × Utilization
Estimated GPUs = Ceiling(Total GPU Work ÷ Effective Work per GPU × (1 + Reserve %))

The model assumes measured runtime remains stable at the entered concurrency and workload scales evenly across GPUs.

What the result means

The result is a rounded estimate of GPUs required to finish the batch within the specified window.

Memory fit and I/O can cap concurrency before compute is saturated, so benchmark the complete pipeline.

Given: 30,000 videos, 54 GPU seconds per video, 3 concurrent videos per GPU, a 10-hour window, 80% utilization, and 15% reserve.

Calculation: Total work = 30,000 × 54 = 1,620,000 GPU-seconds. Effective work per GPU = 36,000 × 3 × 0.80 = 86,400 video-seconds. Base demand = 1,620,000 ÷ 86,400 = 18.75. Reserved = 18.75 × 1.15 = 21.56.

Result: Round up to 22 GPUs.

What does concurrent videos per GPU mean?

It is the tested number of videos processed simultaneously on one GPU, not the total batch size.

Should decoding time be included?

Include it when decoding consumes the same GPU or limits the measured end-to-end pipeline.

Can this compare different GPU models?

Yes. Run separate scenarios using benchmark runtime and concurrency for each hardware type.

Why can adding concurrency stop helping?

Memory pressure, cache contention, decoding, and I/O can reduce throughput or increase failures beyond an optimal point.

Does reserve replace high-availability planning?

No. Reserve is a capacity allowance; architecture still needs explicit failure domains and recovery procedures.