What GPU do I need to run theainerd/Wav2Vec2-large-xlsr-hindi?
316M parameters, published in F32. View on Hugging Face
Wav2Vec2-large-xlsr-hindi is published by theainerd on Hugging Face, with 1,017,691 downloads and 13 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.
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 Wav2Vec2-large-xlsr-hindi at its published (F32) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/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.
More theainerd models
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- gpt2 (137M, F32)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen2.5-7B-Instruct (7.6B, BF16)
