What GPU do I need to run LatitudeGames/Wayfarer-12B?
A 12.2B language model for chat and instruction-following. 12.2B parameters, published in BF16. View on Hugging Face
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.
What Wayfarer-12B is
Wayfarer-12B is a 12.2B-parameter language model published by Latitude Games on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Wayfarer-12B's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 22.8 GB | 27.4 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr | ||
| FP8 (quantized) | 11.4 GB | 13.7 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 5.7 GB | 6.8 GB | RTX 5060 Ti | 1 | $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 4080 Super, at $0.338/hr per GPU ($0.338/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 4080 Super at $0.338/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run Wayfarer-12B
Run Wayfarer-12B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve LatitudeGames/Wayfarer-12B --tensor-parallel-size 1Run Wayfarer-12B with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Wayfarer-12B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Wayfarer-12B-GGUFDeploy Wayfarer-12B on Aquanode
Aquanode has no one-click deploy template for Wayfarer-12B; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 4080 Super or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
