What GPU do I need to run AuroraAI-Research/AuroraIMG2-100M?
101M parameters, published in F32. View on Hugging Face
Set up AuroraIMG2-100MAuroraIMG2-100M is published by AuroraAI-Research on Hugging Face, with 135 downloads and 23 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 | 0.4 GB | 0.4 GB | V100 | 1 | $0.110/hr |
| FP8 (quantized) | 0.1 GB | 0.1 GB | RTX 4070 | 1 | $0.154/hr |
| INT4 (quantized) | 0.0 GB | 0.1 GB | RTX 3060 | 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 AuroraIMG2-100M at its published (F32) precision: 1× V100, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
AuroraIMG2-100M: common questions
How much VRAM does AuroraIMG2-100M need?
0.4 GB at FP32, 0.1 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 0.4 GB of weights plus inference overhead is the whole requirement.
Can AuroraIMG2-100M run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 0.4 GB, or 0.4 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 0.2 GB, or 0.2 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of AuroraIMG2-100M fit on one V100?
35, by VRAM alone. That card carries 16.0 GB and one copy needs 0.4 GB at FP32, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 35 copies is not 35 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More on AuroraIMG2-100M
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Best image generation models: how AuroraIMG2-100M ranks against the rest.
Related reading: V100 pricing and specs, Fast diffusion inference on GPU VMs, and What AI inference is.
