What GPU do I need to run NbAiLab/nb-wav2vec2-1b-bokmaal-v2?
963M parameters, published in F32. View on Hugging Face
Set up nb-wav2vec2-1b-bokmaal-v2nb-wav2vec2-1b-bokmaal-v2 is published by NbAiLab on Hugging Face, with 758,349 downloads and 0 likes to date. It's a Wav2Vec2ForCTC 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 | 3.6 GB | 4.3 GB | P4 | 1 | $0.069/hr |
| FP8 (quantized) | 0.9 GB | 1.1 GB | RTX 5060 Ti | 1 | $0.160/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 nb-wav2vec2-1b-bokmaal-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.
nb-wav2vec2-1b-bokmaal-v2: common questions
How much VRAM does nb-wav2vec2-1b-bokmaal-v2 need?
4.3 GB at FP32, 1.1 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 3.6 GB of weights plus inference overhead is the whole requirement.
Can nb-wav2vec2-1b-bokmaal-v2 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 3.6 GB, or 4.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 1.8 GB, or 2.2 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Wav2Vec 2.0 models
- nb-wav2vec2-1b-nynorsk (963M, F32)
- nb-wav2vec2-1b-bokmaal (963M, F32)
- my_zh_CN_asr_cv13_model (319M, F32)
- wav2vec2-large-xlsr-korean (317M, F32)
- wav2vec2-large-xlsr-53-amharic (316M, F32)
Alternatives at this size
Other models for automatic-speech-recognition within about a third of nb-wav2vec2-1b-bokmaal-v2's 963M parameters, from other model lines.
- whisper-large-v3-turbo (809M, F16)
- parakeet-ctc-1.1b (1.1B, F32)
- Qwen3-ASR-0.6B (938M, BF16)
- distil-large-v3 (756M, F16)
- mms-1b-all (965M, F32)
More on nb-wav2vec2-1b-bokmaal-v2
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Related reading: H100 pricing and specs, Whisper variants compared, What AI inference is, and What AI model fine-tuning is.
