What GPU do I need to run stepfun-ai/GOT-OCR-2.0-hf?
561M parameters, published in BF16. View on Hugging Face
GOT-OCR-2.0-hf is published by stepfun-ai on Hugging Face, with 167,267 downloads and 240 likes to date. It's a GotOcr2ForConditionalGeneration model built for image-text-to-text, published natively in BF16.
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.
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 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run GOT-OCR-2.0-hf at its published (BF16) precision: 1× RTX 3070 on simplepod, at $0.050/hr per GPU ($0.050/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More stepfun-ai models
- GOT-OCR2_0 (716M, BF16)
- Step-3.5-Flash (199.4B, BF16)
- step3 (321.0B, BF16)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
