What GPU do I need to run meta-llama/Llama-3.1-70B-Instruct?
70.6B parameters, published in BF16. View on Hugging FaceGated
Set up Llama-3.1-70B-InstructLlama-3.1-70B-Instruct is published by meta-llama on Hugging Face, with 468,561 downloads and 961 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-3.1-70B-Instruct at its published (BF16) precision: 1× AMD MI300X on runpod, at $2.39/hr per GPU ($2.39/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Llama-3.1-70B-Instruct: common questions
Can Llama-3.1-70B-Instruct run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 157.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 192.0 GB AMD MI300X on runpod at $2.39/hr.
Do I need approval to download Llama-3.1-70B-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 157.7 GB the model needs once you have them.
Does quantizing Llama-3.1-70B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one AMD MI300X on runpod at $2.39/hr. At INT4 (quantized) it drops to one RTX 8000 on akash at $0.221/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)
