What GPU do I need to run datalab-to/chandra-ocr-2?
5.3B parameters, published in BF16. View on Hugging Face
Set up chandra-ocr-2chandra-ocr-2 is published by datalab-to on Hugging Face, with 2,938,989 downloads and 483 likes to date. It's a Qwen3_5ForConditionalGeneration 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 9.9 GB | 11.8 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 4.9 GB | 5.9 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 2.5 GB | 3.0 GB | RTX 3070 | 1 | $0.088/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 chandra-ocr-2 at its published (BF16) precision: 1× RTX 3060, 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.
chandra-ocr-2: common questions
Does chandra-ocr-2 fit on a 12 GB GPU?
Yes. At BF16 it needs 11.8 GB of VRAM, so a 12 GB card holds it with 0.2 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM chandra-ocr-2 can run in?
3.0 GB, at INT4 (quantized), which fits a 6 GB card, against 11.8 GB at BF16. 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 chandra-ocr-2 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 3060 at $0.110/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More datalab-to models
- surya-ocr-2 (686M, BF16)
- lift (9.7B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
