What GPU do I need to run qihoo360/fg-clip-base?
150M parameters, published in F32. View on Hugging Face
Set up fg-clip-basefg-clip-base is published by qihoo360 on Hugging Face, with 18,627 downloads and 10 likes to date. It's a FGCLIPModel model built for zero-shot-image-classification, published natively in F32.
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) |
|---|---|---|---|---|---|
| FP32 | 0.6 GB | 0.7 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.1 GB | 0.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 0.1 GB | 0.1 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 fg-clip-base at its published (F32) 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.
fg-clip-base: common questions
How much VRAM does fg-clip-base need?
0.7 GB at FP32, 0.2 GB at FP8 (quantized), 0.1 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 0.6 GB of weights plus inference overhead is the whole requirement.
Can fg-clip-base run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 0.6 GB, or 0.7 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 0.3 GB, or 0.3 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of fg-clip-base fit on one V100?
23, by VRAM alone. That card carries 16.0 GB and one copy needs 0.7 GB at FP32, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 23 copies is not 23 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.
