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The useful comparison is time and expense for the same completed transcription, not a universal hardware speed multiplier.
Compare Whisper on CPU and GPU using the same audio files, model and comparable settings. The main outcomes are time to a completed transcript, text quality and the full expense of processing. No universal acceleration factor describes every processor, GPU, language and implementation.
Fix the work being compared
Record model version, language handling, decoding settings, file format and expected output. Comparing different models or clean speech with noisy technical audio does not isolate the hardware difference. Preserve the permitted control files and reference transcripts for later changes.
Whisper’s repository notes that speed depends on speech, language and hardware. Report the complete tested systems, including CPU, RAM, disk and GPU. A device-only label can hide a different decoding or storage bottleneck around the model.
Calculate real-time factor explicitly
Here, real-time factor is processing time divided by audio duration. A ten-minute recording processed in two minutes has RTF 0.2. This is an illustrative calculation, not an observed Whisper result on any particular device. State which interval your numerator includes.
| Measurement | CPU and GPU comparison |
|---|---|
| Cold startup | Measure preparation separately |
| Already-loaded processing | Measure repeated work |
| Complete job duration | Capture the user-visible result |
| RTF | Calculate for the same file and boundary |
| Transcript errors | Check useful quality |
A lower model-only RTF can coexist with a longer complete job if other stages dominate. Keep both observations rather than presenting the most favorable interval as the whole workflow.
Separate preparation from execution
Input decoding, disk reads, weight loading and writing output all consume time. The selected transcription implementation and version determine which settings apply. Do not transfer arguments from another library merely because it uses a model called Whisper.
Measure a representative batch as well as a single file. Record concurrency and CPU thread use so background work or oversubscription can be explained. Inspect transcript and timestamp quality before treating two outputs as equivalent completed work.
Price the completed workload
For cloud resources, use the actual billed resource conditions and the measured occupied period. For owned equipment, state which operational costs the estimate includes. Keep these assumptions explicit; a faster GPU does not automatically establish lower total cost for a small or intermittent task.
Choose a bounded conclusion for the tested files and quality target. Funding GPU purchase or operations can then be discussed against an observed workload, rather than a claimed universal CPU-to-GPU multiplier or promised financial return.
Official references: github.com — whisper, github.com — transcribe.py.
Related guides: Choosing GPU memory for Whisper transcription, Running Whisper in a GPU container: a preflight checklist.