Which quant should I pick?

Pick your GPU and a model, and we'll rank its quants from highest to lowest quality — with the fit verdict, size, an estimated speed, and a plain-English note on what you give up at each step down. No account needed.

1 · Your GPU

  • No matching GPU in the list — you can type the model manually.

Search for your card above (Apple Silicon routes to unified memory automatically). Without a GPU we'll still show sizes and the quality guidance below.

Used to judge partial CPU offload when a quant overflows VRAM.

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Your rig lives in this browser. A free account keeps it across the calculator, model pages and this helper.

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2 · Model & context

Lower precision shrinks the KV cache (fits more context).

dots.ocr quants, ranked

Full model page & downloads →
Pick your GPU above to see which of these fit, plus estimated speeds. Sizes and the quality guidance below don't need a rig.
  1. safetensors · BF16 Community default
    5.66 GB

    Full 16-bit (bfloat16) weights: no quantization loss, but about twice an 8-bit quant's size.

    Note: BF16 source weights (rednote-hilab/dots.ocr, MIT-based license) — reference multilingual document-OCR VLM. Community GGUFs are generic auto-quant on a custom arch; safetensors is the reliable format (Transformers/vLLM). Full repo bundled.

Verdicts and sizes match this model's page exactly. Speeds are roofline estimates (a range, not a promise). Quality notes reflect community experience, not measured benchmarks.