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AI Code Review GPU Requirements Estimator

This estimator calculates the number of GPUs needed to support an ai code review workload from task volume, tokens per task, effective GPU throughput, utilization, and redundancy. It converts business demand into an average token rate, then adds practical headroom instead of assuming every GPU can run at its theoretical peak.

The estimate helps teams size serving capacity for automated pull-request analysis and review-comment generation. Actual requirements depend on model size, quantization, batch behavior, sequence length, memory limits, and the serving stack.

Calculator inputs

tasks/h
tokens
tok/s
%
%
Result
—
Calculated result
Peak token demand—
Usable rate per GPU—
Base GPU count—
Provisioned GPUs—

1. Enter peak workload

Use the busiest sustained hour rather than a daily average.

2. Enter token demand per task

Include all input and output tokens processed by the serving fleet.

3. Add measured GPU throughput

Use an effective benchmark for the chosen model, hardware, precision, sequence length, and batching setup.

4. Set utilization

Choose the share of measured throughput you are willing to plan against.

5. Add resilience

Enter extra capacity for bursts, failures, rolling updates, and forecast uncertainty.

6. Review provisioned GPUs

The result rounds up because partial GPUs cannot normally be provisioned independently.

Peak token demand (tokens/s) = Tasks per hour × Tokens per task ÷ 3,600
Usable GPU throughput = Measured GPU throughput × Utilization %
Base GPUs = Peak token demand ÷ Usable GPU throughput
Provisioned GPUs = Ceiling(Base GPUs × (1 + Headroom %))

This is a throughput-based estimate. A separate memory check is still required to confirm that the model weights and KV cache fit on the selected hardware.

What the result means

The main result is the whole number of GPU devices to provision after headroom.

Use measured workload data and current provider or hardware benchmarks when available; production results may vary.

Given: 700 peak tasks/hour, 12,800 tokens/task, 1,300 effective tokens/s per GPU, 70% utilization, and 25% headroom.

Calculation: Demand = 700 × 12,800 ÷ 3,600 = 2,488.9 tokens/s. Usable rate = 1,300 × 0.70 = 910.0 tokens/s. Base GPUs = 2,488.9 ÷ 910.0 = 2.74. With headroom: 2.74 × 1.25 = 3.42, rounded up.

Result: Provision approximately 4 GPUs, subject to memory-fit and benchmark validation.

Should I use theoretical GPU throughput?

No. Use measured end-to-end serving throughput for the intended model and workload. Theoretical hardware specifications usually overstate application-level performance.

Does this estimate include model replicas?

The headroom field can cover additional replicas, but you should explicitly check whether your availability policy requires a minimum replica count across zones or hosts.

How does batching change GPU needs?

Efficient batching can improve tokens per second, while strict low-latency limits may reduce batch efficiency. Enter a benchmark measured at the latency target you must meet.

What about input and output token differences?

If prefill and decoding have materially different throughput, calculate them separately or use a blended effective rate from a representative workload trace.

Can a multi-GPU model use this result directly?

Treat the result as total GPU devices. Then round deployment groups to the tensor- or pipeline-parallel size required by the model.