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LLM Batch Processing Latency Capacity Estimator

The LLM Batch Processing Latency Capacity Estimator predicts how long a finite batch will take with a specified number of parallel workers. It reduces nominal worker count by an efficiency factor, calculates active processing time from average task duration, adds setup and finalization overhead, and estimates the worker count needed to finish before a deadline.

This calculator is useful for overnight jobs, backfills, evaluations, and service-level planning where total makespan matters more than interactive response latency. The linear model assumes tasks are reasonably balanced and workers remain supplied with work. Stragglers, rate limits, checkpointing, retries, and uneven token lengths can extend the tail, so validate the average task time and efficiency with a pilot run.

Batch completion assumptions

tasks
s
workers
%
h
min
Result
—
estimated batch completion time
Active processing time—
Workers required for deadline—
Effective worker count—
Deadline status—

1. Enter batch size
Provide the total number of tasks in the finite job.

2. Measure task duration
Use average active processing seconds per task under the planned configuration.

3. Set parallelism
Enter the number of tasks that can be processed concurrently.

4. Apply efficiency
Reduce ideal parallelism for idle time, imbalance, communication, and scheduler overhead.

5. Add fixed overhead
Include job startup, data staging, and finalization minutes.

6. Compare with deadline
Review predicted completion time and the worker count needed to satisfy the target.

Effective workers = Workers × Parallel efficiency Active hours = Tasks × Seconds per task ÷ Effective workers ÷ 3,600 Completion hours = Active hours + Setup minutes ÷ 60

The deadline worker estimate solves the same formula for workers after reserving time for setup.

What the result means

The main result is estimated wall-clock makespan from job start through finalization.

Estimates depend on the model, serving stack, hardware, quantization, batching strategy, and workload mix. Validate the result with measurements from your own environment before committing capacity or budget.

Given

  • 250,000 tasks
  • 1.8 seconds per task
  • 120 workers
  • 80% efficiency
  • 12 minutes setup

Calculation

Effective workers = 120 × 0.80 = 96. Active time = 250,000 × 1.8 ÷ 96 ÷ 3,600 = 1.3021 hours. Total = 1.3021 + 0.2 = 1.5021 hours.

Result

About 1.50 hours.

The job is estimated to finish within a two-hour deadline under the stated averages.

Why is parallel efficiency below 100%?

Workers may wait for data, hit rate limits, receive uneven tasks, or spend time coordinating. Efficiency captures these non-ideal effects.

Should retries be added to task count or task time?

Either method can work, but avoid double counting. Increase task count for repeated records or increase average time when retries are embedded in measured duration.

What if task lengths vary widely?

Use a lower efficiency or model task groups separately. Long-tail tasks can keep a few workers busy after most of the batch has completed.

Can setup time exceed the deadline?

The calculator will show that the deadline cannot be met through processing parallelism alone. Reduce setup overhead or extend the deadline.

How is batch latency different from inference latency?

Inference latency describes one request. Batch latency here is total job completion time across many tasks and parallel workers.