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Available now on ModelsLab · Language Model

Google: Gemma 2 9BEfficient reasoning. Open weights.

Try Google: Gemma 2 9BAPI Documentation

Deploy Fast. Scale Smart.

Class-Leading Performance

Outperforms Llama 3 8B

Delivers results across reasoning, knowledge, and code generation benchmarks.

Inference Efficiency

Single GPU Deployment

Runs full precision on H100, A100, or TPU with minimal computational overhead.

Versatile Applications

Content to Code Generation

Handles poetry, copywriting, summarization, question answering, and chatbot workflows.

Examples

See what Google: Gemma 2 9B can create

Copy any prompt below and try it yourself in the playground.

Product Description

“Write a compelling product description for a minimalist wireless headphone. Include key features like 30-hour battery life, active noise cancellation, and premium materials. Keep it under 150 words.”

Code Generation

“Generate a Python function that validates email addresses using regex. Include error handling and return True for valid emails, False otherwise.”

Content Summarization

“Summarize the following technical documentation into 3 key takeaways for a developer audience: [paste documentation]. Focus on practical implementation details.”

Reasoning Task

“A store sells apples at $2 each and oranges at $3 each. If someone buys 5 apples and 4 oranges, what's the total cost? Show your work step-by-step.”

For Developers

A few lines of code.
Nine billion parameters. Three lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Google: Gemma 2 9B

Read the docs

Gemma 2 9B is Google's open-source language model with 9 billion parameters, built on Gemini research. It excels at text generation, reasoning, and code tasks while maintaining efficiency for deployment on consumer hardware.

Gemma 2 9B outperforms Llama 3 8B on multiple benchmarks including MMLU (71.3%), HellaSwag (81.9%), and code generation (40.2% HumanEval pass@1). The larger 27B variant is competitive with models 2-3x its size.

The model uses Grouped-Query Attention for efficiency, Rotary Position Embeddings for positional encoding, and interleaved attention alternating between 4096-token sliding windows and 8192-token global context. It was trained on 8 trillion tokens.

Yes. The model runs on a single NVIDIA H100, A100, or Google TPU at full precision. It also supports quantization (4-bit, 8-bit) for deployment on laptops and personal cloud infrastructure.

Primary use cases include content creation (blogs, marketing copy), chatbots and virtual assistants, code generation, document summarization, question answering, and reasoning tasks for research and education.

Yes. Gemma 2 9B is fully open-source with open weights available on Hugging Face. Both pre-trained and instruction-tuned variants are available for commercial and research use.

Ready to create?

Start generating with Google: Gemma 2 9B on ModelsLab.

Try Google: Gemma 2 9BAPI Documentation