What GPU do I need to run llava-hf/llava-1.5-13b-hf?
13.4B parameters, published in F16. View on Hugging Face
llava-1.5-13b-hf is published by llava-hf on Hugging Face, with 151,909 downloads and 35 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.
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 llava-1.5-13b-hf at its published (F16) precision: 1× V100 on simplepod, at $0.170/hr per GPU ($0.170/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 llava-hf models
- llava-1.5-7b-hf (7.1B, F16)
- llava-onevision-qwen2-0.5b-ov-hf (894M, F16)
- llava-v1.6-mistral-7b-hf (7.6B, F16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
