What GPU do I need to run meta-llama/Llama-3.2-1B-Instruct?
1.2B parameters, published in BF16. View on Hugging FaceGated
Set up Llama-3.2-1B-InstructLlama-3.2-1B-Instruct is published by meta-llama on Hugging Face, with 6,693,739 downloads and 1,597 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, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
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-3.2-1B-Instruct at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3.2-1B-Instruct: common questions
How much VRAM does Llama-3.2-1B-Instruct need?
2.8 GB at BF16, 1.4 GB at FP8 (quantized), 0.7 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 2.3 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download Llama-3.2-1B-Instruct?
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 2.8 GB the model needs once you have them.
How many copies of Llama-3.2-1B-Instruct fit on one RTX 3070?
2, by VRAM alone. That card carries 8.0 GB and one copy needs 2.8 GB at BF16, on a live rate of $0.050/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More meta-llama models
- 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)
- Meta-Llama-3-8B (8.0B, BF16)
