What GPU do I need to run llava-hf/llava-1.5-7b-hf?
7.1B parameters, published in F16. View on Hugging Face
Set up llava-1.5-7b-hfllava-1.5-7b-hf is published by llava-hf on Hugging Face, with 2,146,415 downloads and 372 likes to date. It's a LlavaForConditionalGeneration model built for image-text-to-text, 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 | 13.2 GB | 15.8 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 6.6 GB | 7.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.3 GB | 3.9 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 llava-1.5-7b-hf 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.
llava-1.5-7b-hf: common questions
Does llava-1.5-7b-hf fit on a 16 GB GPU?
Yes. At FP16 it needs 15.8 GB of VRAM, so a 16 GB card holds it with 0.2 GB to spare. A 12 GB card is not enough for it at FP16.
What is the least VRAM llava-1.5-7b-hf can run in?
3.9 GB, at INT4 (quantized), which fits a 6 GB card, against 15.8 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 llava-hf models
- llava-onevision-qwen2-0.5b-ov-hf (894M, F16)
- llava-v1.6-mistral-7b-hf (7.6B, F16)
- llava-1.5-13b-hf (13.4B, F16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
