What GPU do I need to run meta-llama/Llama-Guard-4-12B?
12.0B parameters, published in BF16. View on Hugging FaceGated Full specs & deploy guide
Set up Llama-Guard-4-12BLlama-Guard-4-12B is published by meta-llama on Hugging Face, with 46,640 downloads and 128 likes to date. It's a Llama4ForConditionalGeneration model built for image-text-to-text, 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 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 | 22.4 GB | 26.8 GB | RTX A6000 | 1 | $0.330/hr |
| cheaper alt. | RTX A4000 | 2 | $0.226/hr | ||
| FP8 (quantized) | 11.2 GB | 13.4 GB | RTX 4080 | 1 | $0.158/hr |
| INT4 (quantized) | 5.6 GB | 6.7 GB | RTX 4070 Super | 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 Llama-Guard-4-12B 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.
Llama-Guard-4-12B: common questions
Does Llama-Guard-4-12B fit on a 32 GB GPU?
Yes. At BF16 it needs 26.8 GB of VRAM, so a 32 GB card holds it with 5.2 GB to spare. A 24 GB card is not enough for it at BF16.
Do I need approval to download Llama-Guard-4-12B?
Yes. meta-llama gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 26.8 GB the model needs once you have them.
What is the least VRAM Llama-Guard-4-12B can run in?
6.7 GB, at INT4 (quantized), which fits an 8 GB card, against 26.8 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 Llama-Guard-4-12B 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.
More meta-llama models
- Llama-3.2-1B-Instruct (1.2B, BF16)
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3-8B-Instruct (8.0B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
- Llama-3.2-1B (1.2B, BF16)
- Llama-2-7b-hf (6.7B, F16)
