What GPU do I need to run openai/whisper-large-v3-turbo?
809M parameters, published in F16. View on Hugging Face
Set up whisper-large-v3-turbowhisper-large-v3-turbo is published by openai on Hugging Face, with 7,189,303 downloads and 3,278 likes to date. It's a WhisperForConditionalGeneration model built for automatic-speech-recognition, published natively in F16.
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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 1.5 GB | 1.8 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.8 GB | 0.9 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.4 GB | 0.5 GB | RTX 3060 | 1 | $0.110/hr |
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 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-v3-turbo at its published (F16) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
whisper-large-v3-turbo: common questions
How much VRAM does whisper-large-v3-turbo need?
1.8 GB at FP16, 0.9 GB at FP8 (quantized), 0.5 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 1.5 GB of weights plus inference overhead is the whole requirement.
How many copies of whisper-large-v3-turbo fit on one V100?
8, by VRAM alone. That card carries 16.0 GB and one copy needs 1.8 GB at FP16, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 8 copies is not 8 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More openai models
- whisper-large-v3 (1.5B, F16)
- whisper-small (242M, F32)
- whisper-medium (764M, F32)
- whisper-large-v2 (1.5B, F32)
- whisper-small.en (242M, F32)
- whisper-medium.en (764M, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
