What GPU do I need to run huggyllama/llama-7b?
6.7B parameters, published in F16. View on Hugging Face
Set up llama-7bllama-7b is published by huggyllama on Hugging Face, with 231,751 downloads and 360 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F16.
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) |
|---|---|---|---|---|---|
| FP16 | 12.6 GB | 15.1 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 6.3 GB | 7.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.1 GB | 3.8 GB | RTX 5060 Ti | 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-7b at its published (F16) precision: 1× V100, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
llama-7b: common questions
Does llama-7b fit on a 16 GB GPU?
Yes. At FP16 it needs 15.1 GB of VRAM, so a 16 GB card holds it with 0.9 GB to spare. A 12 GB card is not enough for it at FP16.
What is the least VRAM llama-7b can run in?
3.8 GB, at INT4 (quantized), which fits a 6 GB card, against 15.1 GB at FP16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama models
- llama-160m (162M, F32)
- AMD-Llama-135m (134M, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
