What GPU do I need to run openai/whisper-large-v2?
1.5B parameters, published in F32. View on Hugging Face
whisper-large-v2 is published by openai on Hugging Face, with 159,387 downloads and 1,806 likes to date. It's a WhisperForConditionalGeneration model built for automatic-speech-recognition, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activations and allocator fragmentation. Speech models don't build the same growing KV-cache a text model does. Memory scales primarily with input audio length. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run whisper-large-v2 at its published (F32) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More openai models
- whisper-large-v3-turbo (809M, F16)
- whisper-large-v3 (1.5B, F16)
- whisper-small (242M, F32)
- whisper-medium (764M, F32)
- whisper-small.en (242M, F32)
- whisper-medium.en (764M, F32)
