LLM
How to deploy Llama-Guard-4-12B on a GPU cloud
A 12B language model for chat and instruction-following. Full specs, license and use cases.
Llama-Guard-4-12B size and hardware requirements
12.0B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
26.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 22.4 GB | 26.8 GB | RTX A6000 | 1 | $0.330/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 |
How to run Llama-Guard-4-12B
Run Llama-Guard-4-12B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve meta-llama/Llama-Guard-4-12B --tensor-parallel-size 1Deploy Llama-Guard-4-12B on Aquanode
Aquanode has no one-click deploy template for Llama-Guard-4-12B; 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 A6000 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 pod