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LLM Batch Processing GPU Requirements Estimator

The LLM Batch Processing GPU Requirements Estimator calculates how many GPUs are needed to complete a token-defined workload inside a fixed processing window. It derives total token work, adjusts each GPU’s available production time, and rounds the result to a required parallel group size so the estimate can respect tensor-parallel or scheduler topology constraints.

Use this calculator for finite backfills, evaluation suites, data labeling assistance, or periodic generation jobs. Effective tokens per second should come from a batch benchmark using the intended model, precision, context distribution, and serving engine. The model assumes total work can be distributed evenly; data-loading bottlenecks, checkpoints, retries, and minimum model-sharding requirements may require additional capacity.

Batch GPU sizing assumptions

tasks
tokens
tok/s
h
%
GPUs
Result
—
GPUs required for the batch window
Unrounded GPU requirement—
Topology-rounded GPU count—
Total batch tokens—
Token capacity in window—

1. Enter task volume
Provide the number of records to finish in the processing window.

2. Set average tokens
Use total input plus output tokens per task.

3. Benchmark one GPU
Enter sustained effective throughput rather than theoretical peak throughput.

4. Choose the window
Specify the wall-clock hours available for the batch.

5. Apply availability
Reserve time for setup, interruptions, checkpoints, or maintenance.

6. Set topology multiple
Enter the GPU group size required by the model or deployment, then review the rounded count.

Total tokens = Tasks × Tokens per task Per-GPU window capacity = GPU tok/s × Window seconds × Availability Raw GPUs = Total tokens ÷ Per-GPU window capacity Required GPUs = ceil(Raw GPUs ÷ Group size) × Group size

What the result means

The main result is the smallest valid GPU group count that can process the estimated tokens within the window.

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

  • 1,000,000 tasks
  • 1,100 tokens per task
  • 700 effective tok/s per GPU
  • 6-hour window
  • 85% availability
  • group size of 2

Calculation

Total = 1,100,000,000 tokens. Per-GPU capacity = 700 × 21,600 × 0.85 = 12,852,000 tokens. Raw GPUs = 1,100,000,000 ÷ 12,852,000 = 85.59. Rounded to groups of 2 = 86.

Result

86 GPUs.

The rounded cluster has slightly more token capacity than the minimum continuous estimate.

Why round to a parallel group size?

Some models or serving layouts require GPUs in fixed groups. Rounding prevents a mathematically sufficient but operationally invalid count.

What should availability include?

Include any portion of the window unavailable for useful token processing, such as startup, checkpointing, pauses, maintenance, or expected interruptions.

Can spot or preemptible GPUs be modeled?

Yes, lower availability and add a reprocessing allowance to the workload if interruptions cause repeated work.

Should I use input or output throughput?

Use an effective blended throughput measured with the same input/output mix as the batch. Separate models may be needed when prefill and decode phases are scheduled independently.

What if the model itself needs more GPUs than the result?

Use at least the model’s minimum valid shard group, even when the workload-based requirement is smaller.