What GPU do I need to run openai/whisper-small?
242M parameters, published in F32. View on Hugging Face
Set up whisper-smallwhisper-small is published by openai on Hugging Face, with 3,006,151 downloads and 586 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 | 0.9 GB | 1.1 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.2 GB | 0.3 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 0.1 GB | 0.1 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-small at its published (F32) 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-small: common questions
How much VRAM does whisper-small need?
1.1 GB at FP32, 0.3 GB at FP8 (quantized), 0.1 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 0.9 GB of weights plus inference overhead is the whole requirement.
Can whisper-small run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 0.9 GB, or 1.1 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 0.5 GB, or 0.5 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of whisper-small fit on one V100?
14, by VRAM alone. That card carries 16.0 GB and one copy needs 1.1 GB at FP32, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 14 copies is not 14 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Whisper models
- whisper-bemba-stt (242M, F32)
- whisper-small.en (242M, F32)
- anime-whisper (756M, F32)
- kotoba-whisper-v2.2 (756M, F32)
- kotoba-whisper-v2.0 (756M, BF16)
Alternatives at this size
Other models for automatic-speech-recognition within about a third of whisper-small's 242M parameters, from other model lines.
- BanglaASR (242M, F32)
- VibeVoice-ASR-BitNet (323M, F32)
- distil-small.en (166M, F16)
- mms-300m-1130-forced-aligner (315M, F32)
- romanian-wav2vec2 (315M, F32)
More on whisper-small
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Best speech-to-text models: how whisper-small ranks against the rest.
Related reading: V100 pricing and specs, Whisper variants compared, and What AI inference is.
