What GPU do I need to run NbAiLab/nb-asr-beta-qwen06b-lunde05?
782M parameters, published in BF16. View on Hugging FaceGated
nb-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.
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 caveat: 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 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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