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Google DeepMind Intermediate

Gemma 3 27B IT

Gemma 3 27B IT is Google's 27B multimodal instruction model with 128K context, text and image input, 140+ languages, function calling and downloadable weights.

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In plain English

What is this model and why does it matter?

Gemma 3 27B IT is Google's 27B instruction-tuned multimodal open-weight model. It supports text and image input, 128K context, more than 140 languages and function calling.

Single-GPU researchMultimodal assistantsLocal inferenceFine-tuningMultilingual applications
Model overview

Gemma 3 27B IT: features, use cases and important details

Gemma 3 27B IT is Google’s 27B instruction-tuned multimodal model for local and cloud deployment.

Gemma 3 27B IT verified specifications

Google documents a 128K context window for the 27B Gemma 3 size, support for text and image input, text output and more than 140 languages. Gemma 3 also supports function calling and structured outputs.

License and deployment

The model is distributed as downloadable weights under the Gemma Terms of Use. It can be deployed through common local inference stacks, Kaggle and cloud model platforms.

Best uses

The 27B instruction-tuned checkpoint is useful for multimodal assistants, multilingual applications, research and fine-tuning where teams want more control than a closed API offers.

Limitations

It still requires substantial GPU memory, uses custom Gemma terms and has static knowledge compared with tool-connected hosted models.

Get started

How to use this model

  1. Accept the Gemma Terms and download the 27B instruction-tuned checkpoint.
  2. Use a supported Transformers or inference runtime.
  3. Provide text and optional image inputs.
  4. Use function calling or structured output for application workflows.
  5. Quantize the model when lower memory use is required.
Copy and try

Example prompts

  • Analyze this image and explain the technical issue.
  • Summarize this multilingual document and return structured findings.
  • Use the provided function schema to select the correct tool.
Capabilities

What it can do

  • 27B parameters
  • 128K context
  • Text and image input
  • 140+ languages
  • Function calling
  • Structured outputs
  • Fine-tuning
Best for

Practical use cases

  • Local multimodal assistants
  • Research
  • Document analysis
  • Multilingual apps
  • Custom fine-tuning
Pricing

What does it cost?

Gemma 3 27B IT is distributed as downloadable weights under the Gemma Terms. There is no Google per-token price for the weights; infrastructure or hosted-provider costs depend on deployment.

Simple summaryThe weights are free to access under Gemma's terms, but local inference still requires capable GPU hardware. Hosted deployment pricing varies by provider.

What stands out

  • Strong size-to-capability ratio
  • 128K context
  • Multimodal
  • Broad language support
  • Downloadable weights

Things to consider

  • Custom Gemma terms rather than Apache/MIT
  • 27B still needs significant VRAM
  • Not a hosted Gemini API identity
Limitations

Important restrictions and trade-offs

  • Static model knowledge
  • Vision tasks can still fail
  • Deployment quality depends on quantization and serving stack
SimplifyAITools verdict

Our editorial take

A highly searched Gemma checkpoint that offers a practical balance of multimodal capability, local deployment and 27B-scale hardware requirements.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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