Model reference · open weights

Ovis2

LLMs ATH-MaaS Vision + text 2 builds Open weights 15k dl/mo

Ovis2 is an open-weight language model from ATH-MaaS. Ovis2-1B (BF16) weighs 2.5 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.

  • Ovis2 is a multi-modal large language model developed by ATH-MaaS for image-text-to-text tasks.
  • The specific Ovis2-1B variant contains 1.3B parameters and supports English and Chinese.
  • It is designed to handle single images, multiple images, and video inputs, and it is released under the Apache 2.0 license.

Summary of the ATH-MaaS/Ovis2-1B model card, 2026-10-01

What it is

Released byATH-MaaS
Released2025-02-10
Parameters1.3B
VRAM2.5 GB for the weights

What it runs on

Memory and cards for Ovis2-1B (BF16)

2.5 GBweights, file size
12 MBcache per 1K tokens
640 MBruntime overhead, at least
32,768 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB8320all 32K11.6 GB
RTX 4060 Ti 16 GB12130all 32K15.4 GB
RTX 3090 24 GB20050all 32K23.4 GB
RTX 4090 24 GB20050all 32K23.4 GB
RTX 5090 32 GB27669all 32K31.0 GB
L40S 48 GB405101all 32K44.0 GB
A100 80 GB745186all 32K78.2 GB
H100 80 GB704176all 32K78.1 GB
RTX PRO 6000 Blackwell 96 GB860215all 32K93.8 GB
DGX Spark (GB10) 128 GB unified995248all 32K107 GB
H200 141 GB1000+324all 32K138 GB
B200 180 GB1000+419all 32K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
13.3 GB3.6 GB
53.7 GB5.2 GB
84.0 GB6.4 GB
164.8 GB9.6 GB
326.4 GB16.1 GB
649.6 GB28.9 GB

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

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
Ovis2-1B (above) ↗ 1.3BBF16 2.5 GBRTX 3060 12 GB
Ovis2-4B ↗ 4.6BBF16 9.2 GBRTX 3060 12 GB

From the model card

What ATH-MaaS says about Ovis2

Read the model card

Introduction

GitHub | Paper

We are pleased to announce the release of Ovis2, our latest advancement in multi-modal large language models (MLLMs). Ovis2 inherits the innovative architectural design of the Ovis series, aimed at structurally aligning visual and textual embeddings. As the successor to Ovis1.6, Ovis2 incorporates significant improvements in both dataset curation and training methodologies.

Key Features:

  • Small Model Performance: Optimized training strategies enable small-scale models to achieve higher capability density, demonstrating cross-tier leading advantages.

  • Enhanced Reasoning Capabilities: Significantly strengthens Chain-of-Thought (CoT) reasoning abilities through the combination of instruction tuning and preference learning.

  • Video and Multi-Image Processing: Video and multi-image data are incorporated into training to enhance the ability to handle complex visual information across frames and images.

  • Multilingual Support and OCR: Enhances multilingual OCR beyond English and Chinese and improves structured data extraction from complex visual elements like tables and charts.

Model Zoo

Ovis MLLMsViTLLMModel WeightsDemo
Ovis2-1Baimv2-large-patch14-448Qwen2.5-0.5B-InstructHuggingfaceSpace
Ovis2-2Baimv2-large-patch14-448Qwen2.5-1.5B-InstructHuggingfaceSpace
Ovis2-4Baimv2-huge-patch14-448Qwen2.5-3B-InstructHuggingfaceSpace
Ovis2-8Baimv2-huge-patch14-448Qwen2.5-7B-InstructHuggingfaceSpace
Ovis2-16Baimv2-huge-patch14-448Qwen2.5-14B-InstructHuggingfaceSpace
Ovis2-34Baimv2-1B-patch14-448Qwen2.5-32B-InstructHuggingface-

Performance

We use VLMEvalKit, as employed in the OpenCompass multimodal and reasoning leaderboard, to evaluate Ovis2.

Image Benchmark

BenchmarkQwen2.5-VL-3BSAIL-VL-2BInternVL2.5-2B-MPOOvis1.6-3BInternVL2.5-1B-MPOOvis2-1BOvis2-2B
MMBench-V1.1test77.173.670.774.165.868.476.9
MMStar56.556.554.952.049.552.156.7
MMMUval51.444.144.646.740.336.145.6
MathVistatestmini60.162.853.458.947.759.464.1
HallusionBench48.745.940.743.834.845.250.2
AI2D81.477.475.177.868.576.482.7
OCRBench83.183.183.880.184.389.087.3
MMVet63.244.264.257.647.250.058.3
MMBenchtest78.67772.876.667.970.278.9
MMT-Benchval60.857.154.459.250.855.561.7
RealWorldQA66.56261.366.75763.966.0
BLINK48.446.443.843.84144.047.9
QBench74.472.869.875.863.371.376.2
ABench75.574.571.175.267.571.376.6
MTVQA24.920.222.621.121.723.725.6

Video Benchmark

BenchmarkQwen2.5-VL-3BInternVL2.5-2BInternVL2.5-1BOvis2-1BOvis2-2B
VideoMME(wo/w-subs)61.5/67.651.9 / 54.150.3 / 52.348.6/49.557.2/60.8
MVBench67.068.864.360.3264.9
MLVU(M-Avg/G-A

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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