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Unlimited-OCR 2 on a 16 GB GPU: it reads as well as Chandra 2, but it is not unlimited

A developer benchmarked Baidu's newly released Unlimited-OCR 2 (3.3B parameters, MIT license) on a 16 GB RTX 4070 Ti SUPER against Chandra 2, Tesseract, Marker 2.0 and DeepSeek-OCR, finding it ties Chandra 2 for accuracy at a 0.12% median character error rate on a 20-page Central Bank of Chile report. However, the model's "unlimited" length claim failed on consumer hardware: under transformers, memory grew from 7.5 to 11.1 GB and per-token time rose 1.49x between 10 and 40 pages, and an 80-page request ran out of memory after 74 minutes. Baidu's modified SGLang kept per-token time flat at roughly 4.17-4.19 ms but hung in 10-page window mode and silently truncated a 40-page document to 24 pages, leading the tester to conclude Marker remains the practical choice for huge files.

by read2 min views1 publishedOct 28, 2026

On October 27 Baidu released Unlimited-OCR 2: 3.3B parameters, MIT license, and a promise that its sparse attention keeps "per-step decoding latency and memory footprint nearly constant regardless of document length". That night I ran it on a 16 GB RTX 4070 Ti SUPER against what I actually used for huge PDFs (Datalab's Marker and Chandra 2, locally), DeepSeek-OCR, Unlimited-OCR 1 and Tesseract, with the decision rules written down before the first run.

| Median character error rate per page | Unlimited-OCR 2 | Chandra 2 | Tesseract | Marker 2.0 | DeepSeek-OCR | | 20 pages of a real Central Bank of Chile report | 0.12 % | 0.31 % | 0.51 % | 2.51 % | 3.12 % |

"Unlimited" does not hold on 16 GB with transformers. From 10 to 40 pages in one request, memory went from 7.5 to 11.1 GB and time per token rose 1.49 times; at 80 pages it ran out of memory after 74 minutes. The transformers path keeps every token in a dynamic cache. #

With Baidu's modified SGLang, time per token stays flat (4.17-4.19 ms from 10 to 40 pages, about nine times faster than transformers). But the 10-page window mode hung after the first window, and a single window silently returned 24 of 40 pages when its fixed cache filled up (the README's--allow-auto-truncate ). #

For huge files, Marker is still the practical choice: the full 74-page report in 12.6 minutes using 4.2 GB of the card. Chandra 2 took 46 minutes and 11 GB.

  • Unlimited-OCR 2 is MIT; Chandra 2's weights are free only under 2 million dollars in funding or revenue.

The post also covers the install stumbles on NixOS (Triton looking for /sbin/ldconfig, Surya spawning Docker with --runtime nvidia), a normalization bug the external audit caught in my own analysis, and the threats to validity.

Read the full measurements, the pre-registered sheet and the scripts → https://efraingaray.com/en/blog/unlimited-ocr-2-16gb/

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