What GPU do I need to run SG161222/RealVisXL_V5.0?
A 2.6B-parameter text-to-image model. 2.6B parameters, published in F32. View on Hugging Face
RealVisXL_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.
What RealVisXL_V5.0 is
RealVisXL_V5.0 is a 2.6B-parameter text-to-image model published by Independent (SG161222) on Hugging Face, released under OpenRAIL++.
License note: permissive with use-based restrictions written into the license (no illegal or harmful-use clauses). Facts in this section are sourced from RealVisXL_V5.0's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Text-to-image generation
- Creative asset generation
- ComfyUI workflows
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 | $0.088/hr |
| FP8 (quantized) | 2.4 GB | 2.9 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.2 GB | 1.4 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 RealVisXL_V5.0 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.
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.
How to run RealVisXL_V5.0
Run RealVisXL_V5.0 with Diffusers (Python)
Generic example using Hugging Face's diffusers library, not from the model's own docs.
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("SG161222/RealVisXL_V5.0", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")Run RealVisXL_V5.0 with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download SG161222/RealVisXL_V5.0's checkpoint into the models folder and load it in a workflow; this is a real Aquanode template, but loading this specific checkpoint is a manual step, not a one-click deploy.
Deploy RealVisXL_V5.0 on Aquanode
Aquanode has no one-click deploy template for RealVisXL_V5.0; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch the ComfyUI template sized to the requirement above (1× V100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI templateWeight-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.
