What GPU do I need to run TIGER-Lab/VLM2Vec-Full?
4.1B parameters, published in BF16. View on Hugging Face
Set up VLM2Vec-FullVLM2Vec-Full is published by TIGER-Lab on Hugging Face, with 219,916 downloads and 29 likes to date. It's a Phi3VForCausalLM model built for text-generation, published natively in BF16.
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 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run VLM2Vec-Full at its published (BF16) precision: 1× RTX 5060 Ti on simplepod, at $0.100/hr per GPU ($0.100/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
VLM2Vec-Full: common questions
Does VLM2Vec-Full fit on a 12 GB GPU?
Yes. At BF16 it needs 9.3 GB of VRAM, so a 12 GB card holds it with 2.7 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM VLM2Vec-Full can run in?
2.3 GB, at INT4 (quantized), which fits a 6 GB card, against 9.3 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing VLM2Vec-Full lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 5060 Ti on simplepod at $0.100/hr. At INT4 (quantized) it drops to one RTX 3070 on simplepod at $0.050/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More TIGER-Lab models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
