What GPU do I need to run LatitudeGames/Wayfarer-12B?

12.2B parameters, published in BF16. View on Hugging Face Full specs & deploy guide

12.2B
Parameters
BF16
Native precision
MistralForCausalLM
Architecture
text-generation
Pipeline

Wayfarer-12B is published by LatitudeGames on Hugging Face, with 102 downloads and 221 likes to date. It's a MistralForCausalLM model built for text-generation, 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)
BF1622.8 GB27.4 GBRTX A60001$0.330/hr
cheaper alt.RTX A40002$0.226/hr
FP8 (quantized)11.4 GB13.7 GBRTX 40801$0.158/hr
INT4 (quantized)5.7 GB6.8 GBRTX 4070 Super1$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 Wayfarer-12B at its published (BF16) precision: 1× RTX A6000, at $0.330/hr per GPU ($0.330/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Wayfarer-12B: common questions

Does Wayfarer-12B fit on a 32 GB GPU?

Yes. At BF16 it needs 27.4 GB of VRAM, so a 32 GB card holds it with 4.6 GB to spare. A 24 GB card is not enough for it at BF16.

What is the least VRAM Wayfarer-12B can run in?

6.8 GB, at INT4 (quantized), which fits an 8 GB card, against 27.4 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.

Does quantizing Wayfarer-12B lower the GPU bill?

Yes. At BF16 the cheapest live fit is one RTX A6000 at $0.330/hr. At INT4 (quantized) it drops to one RTX 4070 Super at $0.110/hr, provided a quantized checkpoint exists for it.

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

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