What GPU do I need to run openai/whisper-large-v2?
1.5B parameters, published in F32. View on Hugging Face
Set up whisper-large-v2whisper-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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 5.7 GB | 6.9 GB | P4 | 1 | $0.069/hr |
| FP8 (quantized) | 1.4 GB | 1.7 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.7 GB | 0.9 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-v2 at its published (F32) precision: 1× P4, at $0.069/hr per GPU ($0.069/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-v2: common questions
Does whisper-large-v2 fit on a 8 GB GPU?
Yes. At FP32 it needs 6.9 GB of VRAM, so an 8 GB card holds it with 1.1 GB to spare. A 6 GB card is not enough for it at FP32.
Can whisper-large-v2 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 5.7 GB, or 6.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.9 GB, or 3.4 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM whisper-large-v2 can run in?
0.9 GB, at INT4 (quantized), which fits a 6 GB card, against 6.9 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Whisper models
- whisper-large (1.5B, F32)
- Breeze-ASR-25 (1.5B, BF16)
- Breeze-ASR-26 (1.5B, F32)
- whisper-large-v3 (1.5B, F16)
- Whisper-Hindi2Hinglish-Prime (1.5B, F32)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for automatic-speech-recognition within about a third of whisper-large-v2's 1.5B parameters, from other model lines.
- parakeet-ctc-1.1b (1.1B, F32)
- cohere-transcribe-03-2026 (2.1B, BF16)
- seamless-m4t-v2-large (2.3B, F32)
- granite-speech-4.1-2b (2.3B, BF16)
- Qwen3-ASR-1.7B-hf (2.0B, BF16)
More on whisper-large-v2
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Best speech-to-text models: how whisper-large-v2 ranks against the rest.
Related reading: H100 pricing and specs, Whisper variants compared, and What AI inference is.
