What GPU do I need to run meta-llama/Llama-2-7b-hf?
6.7B parameters, published in F16. View on Hugging FaceGated
Llama-2-7b-hf is published by meta-llama on Hugging Face, with 743,903 downloads and 2,356 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F16, 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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run Llama-2-7b-hf at its published (F16) precision: 1× V100 on simplepod, at $0.060/hr per GPU ($0.060/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
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 (8.0B, BF16)
- Meta-Llama-3-8B-Instruct (8.0B, BF16)
- Llama-3.2-1B (1.2B, BF16)
- Llama-3.2-3B-Instruct (3.2B, BF16)
