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Published Whisper memory figures are approximate starting points, not guarantees for every implementation or simultaneous workload.
Whisper GPU requirements depend primarily on the model, implementation and number of recordings processed at once. Start with the official repository’s approximate memory guidance, then test your own audio and the complete processing path. A card’s nominal VRAM is not a guarantee for every transcription configuration.
Read memory guidance in context
The official Whisper table checked on November 1, 2026 lists approximate VRAM requirements of 1 GB for tiny and base, 2 GB for small, 5 GB for medium, 10 GB for large and 6 GB for turbo. These are starting points for the documented implementation, not measured results for your server.
Record the exact model and software version. A different implementation, numeric format, batch or concurrent workload can change the resource profile. Do not select a card by matching the approximate figure exactly while ignoring other processes and runtime requirements.
Describe the audio task
| Property | Example to record | Why it matters |
|---|---|---|
| Language | Speech with domain terminology | Model choice and evaluation |
| Duration | Distribution of file lengths | Queue planning |
| Audio quality | Noise and microphone variation | Error checking |
| Concurrency | Active transcription jobs | Resource peaks |
| Output | Text and timestamps | End-to-end validation |
Use permitted recordings with a known reference or reviewed transcript. Include the task’s difficult speech rather than only clean demonstration audio. A larger model or GPU is useful only if the resulting quality and waiting time meet the need.
Measure stages and shared load
Separate decoding, model loading, transcription and result writing. Observe GPU memory alongside the job timeline, using device monitoring and application logs. Without time markers, another process’s peak can be mistaken for transcription demand.
First test one recording, then the intended parallel workload. Several independently successful jobs may exceed a shared device’s usable resources. Include RAM, storage and CPU observations because decoding or file handling can constrain the complete pipeline even when the GPU is available.
Select by completed, useful results
Save total elapsed time, transcript quality, timestamps where required and the durable output location. Repeat a controlled run after restart to confirm the environment and model artifacts can be recovered. Keep cold-start and already-loaded measurements distinct.
For a hardware owner, the result is a specific transcription profile rather than a universal claim that a card runs Whisper. An operations partner can relate additional memory or replicas to an observed workload.
Official references: github.com — whisper, docs.nvidia.com — index.
Related guides: Qwen rerankers: separating relevance scoring from text generation, Whisper on CPU or GPU: comparing completion time and cost.