What GPU do I need to run meta-llama/Llama-3.1-70B?
70.6B parameters, published in BF16. View on Hugging FaceGated
Set up Llama-3.1-70BLlama-3.1-70B is published by meta-llama on Hugging Face, with 45,201 downloads and 435 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 131.4 GB | 157.7 GB | RTX A5000 | 7 | $1.23/hr |
| FP8 (quantized) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 5 | $0.550/hr | ||
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/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-3.1-70B at its published (BF16) precision: 7× RTX A5000, at $0.176/hr per GPU ($1.23/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: common questions
Can Llama-3.1-70B run on a single GPU?
No. At BF16 it needs 157.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 7 of them.
Do I need approval to download Llama-3.1-70B?
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.
How many GPUs do I need to run Llama-3.1-70B?
7 at BF16. It needs 157.7 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 7 of them come to $1.23/hr in total.
Does quantizing Llama-3.1-70B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 7 RTX A5000 cards at $1.23/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-70B-Instruct (70.6B, BF16)
- Llama-3.1-70B-Instruct-FP8 (70.6B, F8_E4M3)
- Llama-3.1-70B-LatamGPT-SFT-1.0 (70.6B, BF16)
- Meta-Llama-3.1-70B (70.6B, BF16)
- Meta-Llama-3.1-70B-Instruct (70.6B, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
