Model reference · open weights

Qari-OCR-VL

LLMs NAMAA-Space Vision + text 1 build Open weights 84k dl/mo

Qari-OCR-VL is an open-weight language model from NAMAA-Space. Qari-OCR-v0.3-VL-2B-Instruct (FP16) weighs 4.4 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • Qari-OCR-VL is a 2.2B parameter vision-language model developed by NAMAA-Space for Arabic Optical Character Recognition and structural document understanding.
  • It is built on Qwen2-VL-2B-Instruct and supports a context length of 32,768 tokens.
  • The model is licensed under apache-2.0 and focuses on preserving document layouts, HTML tags, and Arabic diacritics.

Summary of the NAMAA-Space/Qari-OCR-v0.3-VL-2B-Instruct model card, 2026-10-01

What it is

Released byNAMAA-Space
Released2025-04-10
Parameters2.2B
VRAM4.4 GB for the weights

What it runs on

Memory and cards for Qari-OCR-v0.3-VL-2B-Instruct (FP16)

4.4 GBweights, file size
29 MBcache per 1K tokens
2.0 GBruntime overhead, at least
32,768 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB225all 32K11.6 GB
RTX 4060 Ti 16 GB389all 32K15.4 GB
RTX 3090 24 GB7218all 32K23.4 GB
RTX 4090 24 GB7218all 32K23.4 GB
RTX 5090 32 GB10426all 32K31.0 GB
L40S 48 GB15939all 32K44.0 GB
A100 80 GB30576all 32K78.2 GB
H100 80 GB28771all 32K78.1 GB
RTX PRO 6000 Blackwell 96 GB35488all 32K93.8 GB
DGX Spark (GB10) 128 GB unified412103all 32K107 GB
H200 141 GB542135all 32K138 GB
B200 180 GB703175all 32K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
16.6 GB7.3 GB
57.6 GB11.1 GB
88.3 GB13.9 GB
1610.1 GB21.4 GB
3213.9 GB36.5 GB
6421.4 GB66.5 GB

One card, with vLLM's small-card settings.

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