What GPU do I need to run NbAiLab/nb-asr-beta-qwen06b-lunde05?
782M parameters, published in BF16. View on Hugging FaceGated
Set up nb-asr-beta-qwen06b-lunde05nb-asr-beta-qwen06b-lunde05 is published by NbAiLab on Hugging Face, with 12,639 downloads and 0 likes to date. It's a Qwen3ASRForConditionalGeneration model built for automatic-speech-recognition, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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) |
|---|---|---|---|---|---|
| BF16 | 1.5 GB | 1.7 GB | RTX 4070 Super | 1 | $0.121/hr |
| FP8 (quantized) | 0.7 GB | 0.9 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.4 GB | 0.4 GB | RTX 4070 Super | 1 | $0.121/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-asr-beta-qwen06b-lunde05 at its published (BF16) precision: 1× RTX 4070 Super, at $0.121/hr per GPU ($0.121/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
nb-asr-beta-qwen06b-lunde05: common questions
How much VRAM does nb-asr-beta-qwen06b-lunde05 need?
1.7 GB at BF16, 0.9 GB at FP8 (quantized), 0.4 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.
Do I need approval to download nb-asr-beta-qwen06b-lunde05?
Yes. NbAiLab gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 1.7 GB the model needs once you have them.
How many copies of nb-asr-beta-qwen06b-lunde05 fit on one RTX 4070 Super?
6, by VRAM alone. That card carries 12.0 GB and one copy needs 1.7 GB at BF16, on a live rate of $0.121/hr for the whole card. Throughput is not modelled here, so 6 copies is not 6 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Alternatives at this size
Other models for automatic-speech-recognition within about a third of nb-asr-beta-qwen06b-lunde05's 782M parameters, from other model lines.
- whisper-large-v3-turbo (809M, F16)
- parakeet-ctc-1.1b (1.1B, F32)
- Qwen3-ASR-0.6B (938M, BF16)
- nemotron-3.5-asr-streaming-0.6b (638M, F32)
- distil-large-v3 (756M, F16)
More on nb-asr-beta-qwen06b-lunde05
Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.
Best speech-to-text models: how nb-asr-beta-qwen06b-lunde05 ranks against the rest.
Related reading: H100 pricing and specs, Whisper variants compared, and What AI inference is.
