Zamba2-VL models offer faster vision-language processing

Researchers have introduced Zamba2-VL, a new family of vision-language models that leverage a hybrid architecture combining Mamba2 state-space layers with transformer blocks. These models demonstrate strong performance across various vision and language tasks, rivaling established transformer-based models like Molmo2 and Qwen3-VL. A key advantage of Zamba2-VL is its significantly faster time-to-first-token, making it particularly suitable for on-device and edge deployments. AI

IMPACT Offers faster inference for vision-language tasks, potentially enabling more responsive on-device AI applications.

RANK_REASON The cluster contains a technical report detailing a new suite of vision-language models released on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Cobra
• Hassan Shapourian
• InternVL3.5
• Mamba2
• mmMamba
• Molmo2
• Qwen3-VL
• VL-Mamba
• Zamba2-VL

• paper
• model release

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