LLM
How to deploy Llama-Guard-3-8B on a GPU cloud
A 8B language model for chat and instruction-following. Full specs, license and use cases.
Llama-Guard-3-8B size and hardware requirements
8.0B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
17.9 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 15.0 GB | 17.9 GB | RTX 3090 | 1 | $0.147/hr |
| FP8 (quantized) | 7.5 GB | 9.0 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 3.7 GB | 4.5 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run Llama-Guard-3-8B
Run Llama-Guard-3-8B 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-3-8B --tensor-parallel-size 1Run Llama-Guard-3-8B with Ollama
Verified against Ollama's own library listing.
ollama run llama-guard3:8bRun Llama-Guard-3-8B with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/Llama-Guard-3-8B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/Llama-Guard-3-8B-GGUFSource: https://huggingface.co/mradermacher/Llama-Guard-3-8B-GGUF
Deploy Llama-Guard-3-8B on Aquanode
Aquanode has no one-click deploy template for Llama-Guard-3-8B; 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 3090 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