AuroraAI-Research models
1 AuroraAI-Research model from AuroraAI-Research on Hugging Face, from 101M to 101M parameters, published by AuroraAI-Research in F32. The smallest official model, AuroraIMG2-100M, needs about 0.4 GB of VRAM at its published precision; the cheapest live fit is P4 at $0.058/hr.
Pick a size
One row per official AuroraAI-Research size: the VRAM it needs at each precision, the cheapest GPU that holds it today, and the KV cache for a 32K-token context.
| Model | Parameters | Native VRAM | FP8 VRAM | INT4 VRAM | Live GPU fit (native) | Est. $/hr | KV cache at 32K |
|---|---|---|---|---|---|---|---|
| AuroraIMG2-100M | 101M | 0.4 GB | 0.1 GB | 0.1 GB | P4 | $0.058/hr | not published for this architecture |
VRAM is weights times a flat 1.2 overhead; the KV cache is a separate per-model figure at 16-bit, shown where the architecture is published. See the methodology.
Official models (1)
| Model | Parameters | Published as | VRAM needed | Live GPU fit | Est. $/hr |
|---|---|---|---|---|---|
| AuroraIMG2-100M | 101M | F32 | 0.4 GB | P4 | $0.058/hr |
VRAM is for the precision the model is published in: the weights times a flat 1.2 overhead, with the KV cache not included. See the methodology. The fit shown is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does.
