What GPU do I need to run SG161222/RealVisXL_V5.0?
2.6B parameters, published in F32. View on Hugging Face Full specs & deploy guide
Set up RealVisXL_V5.0RealVisXL_V5.0 is published by SG161222 on Hugging Face, with 372,127 downloads and 234 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in F32.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache. The real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 9.6 GB | 11.5 GB | V100 | 1 | $0.060/hr |
| FP8 (quantized) | 2.4 GB | 2.9 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 1.2 GB | 1.4 GB | A16 | 1 | $0.059/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 RealVisXL_V5.0 at its published (F32) precision: 1× V100, at $0.060/hr per GPU ($0.060/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
RealVisXL_V5.0: common questions
Does RealVisXL_V5.0 fit on a 12 GB GPU?
Yes. At FP32 it needs 11.5 GB of VRAM, so a 12 GB card holds it with 0.5 GB to spare. An 8 GB card is not enough for it at FP32.
Can RealVisXL_V5.0 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 9.6 GB, or 11.5 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 4.8 GB, or 5.7 GB with overhead. That moves it onto a 6 GB card instead of a 12 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM RealVisXL_V5.0 can run in?
1.4 GB, at INT4 (quantized), which fits a 6 GB card, against 11.5 GB at FP32. 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 RealVisXL_V5.0 lower the GPU bill?
Yes. At FP32 the cheapest live fit is one V100 at $0.060/hr. At INT4 (quantized) it drops to one A16 at $0.059/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More SG161222 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)
