What GPU do I need to run meta-llama/Llama-Guard-3-1B?
1.5B parameters, published in BF16. View on Hugging FaceGated
Set up Llama-Guard-3-1BLlama-Guard-3-1B is published by meta-llama on Hugging Face, with 31,249 downloads and 114 likes to date. It's a LlamaForCausalLM model built for text-generation, 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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 2.8 GB | 3.3 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 1.4 GB | 1.7 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 0.7 GB | 0.8 GB | RTX 3060 | 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-3-1B at its published (BF16) precision: 1× RTX 3060, at $0.110/hr per GPU ($0.110/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-3-1B: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for Llama-Guard-3-1B is not published for this architecture, so no figure is shown.
Llama-Guard-3-1B: common questions
How much VRAM does Llama-Guard-3-1B need?
3.3 GB at BF16, 1.7 GB at FP8 (quantized), 0.8 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 2.8 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download Llama-Guard-3-1B?
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 3.3 GB the model needs once you have them.
How many copies of Llama-Guard-3-1B fit on one RTX 3060?
3, by VRAM alone. That card carries 12.0 GB and one copy needs 3.3 GB at BF16, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 3 copies is not 3 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama Guard models
- Llama-Phishsense-1B (1.2B, BF16)
- Llama-Guard-3-8B (8.0B, BF16)
- Llama-Guard-4-12B (12.0B, BF16)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for text-generation within about a third of Llama-Guard-3-1B's 1.5B parameters, from other model lines.
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
- OpenELM-1_1B-Instruct (1.1B, BF16)
- OLMo-2-0425-1B (1.5B, F32)
- MiniCPM5-1B (1.1B, BF16)
- LFM2.5-1.2B-Instruct (1.2B, BF16)
More on Llama-Guard-3-1B
Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.
Related reading: H100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
