Vision

Check exact model cardOpen weights where releasedUpdated August 2026

PaliGemma 2

PaliGemma 2 is a Gemma-adjacent vision-language model family for multimodal experiments.

Google · Gemma

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (README.md) (google/paligemma2-3b-pt-224), Hugging Face

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Vision-language app prototypes and multimodal evaluation.

Who should use it

  • Vision-language app prototypes and multimodal evaluation.
  • Builders who want local or self-hosted testing options.

Common workflows

  • Vision-language workflows
  • vision workflows
  • multimodal workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Gemma workflows.
  • Vision-language app prototypes and multimodal evaluation.
  • Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: 128 tokens (text input/output — pretrained for short image-captioning tasks, not chat-length context).
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Sources to verify

Hugging Face model card (README.md) (google/paligemma2-3b-pt-224)Hugging Face

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Gemma family hubAI tools directoryModel selection guideModel source notesModels directory
HardwareVaries by size; the 3B 224px checkpoint needs ~1.8 GB at Q4_K_M (3B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext128 tokens (text input/output — pretrained for short image-captioning tasks, not chat-length context)Last updated2026
Hugging Face model card (README.md) (google/paligemma2-3b-pt-224) →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Gemma family hub →CodeGemma 7B →Gemma 2 27B Instruct →Gemma 2 2B Instruct →Phi-3.5 Vision →Pixtral 12B →Gemma 3 12B →

Guides, stacks, and comparisons

AI tools directory →Model selection guide →All model comparisons →

Related models

CodeGemma 7BCoding assistant experiments and developer workflow prototypes.Gemma 2 27B InstructEfficient local prototypes, app workflows, and Gemma-family comparisons.Gemma 2 2B InstructEfficient local prototypes, app workflows, and Gemma-family comparisons.Phi-3.5 VisionSmall vision-language experimentsPixtral 12BVision-language local and hosted experimentsGemma 3 12B12B open-weight model in the Gemma family.

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