What GPU do I need to run fishaudio/s2-pro?

4.6B parameters, published in BF16. View on Hugging Face

4.6B
Parameters
BF16
Native precision
Unknown
Architecture
text-to-speech
Pipeline

s2-pro is published by fishaudio on Hugging Face, with 315,514 downloads and 1,321 likes to date. It's a unlisted-architecture model built for text-to-speech, published natively in BF16.

VRAM required & cheapest live GPU fit

Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.

PrecisionWeight sizeRequired VRAMCheapest live fitGPUs neededEst. $/hr (full fit)
BF168.5 GB10.2 GBRTX 5060 Ti1$0.110/hr
FP8 (quantized)4.2 GB5.1 GBRTX 5060 Ti1$0.110/hr
INT4 (quantized)2.1 GB2.5 GBRTX 5060 Ti1$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 s2-pro at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

s2-pro: common questions

Does s2-pro fit on a 12 GB GPU?

Yes. At BF16 it needs 10.2 GB of VRAM, so a 12 GB card holds it with 1.8 GB to spare. An 8 GB card is not enough for it at BF16.

What is the least VRAM s2-pro can run in?

2.5 GB, at INT4 (quantized), which fits a 6 GB card, against 10.2 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

fishaudio models

Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.

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