--- title: Gemma-2 Instruct 27B — Powerful LLM | ModelsLab description: Access Gemma-2 Instruct (27B) API for efficient inference on tasks like reasoning and summarization. Try Gemma-2 Instruct (27B) model now. url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/gemma-2-instruct-27b canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/gemma-2-instruct-27b type: website component: Seo/ModelPage generated_at: 2026-05-13T09:42:29.750741Z --- Available now on ModelsLab · Language Model Gemma-2 Instruct (27B) Scale Reasoning Efficiently --- [Try Gemma-2 Instruct (27B)](/models/google_deepmind/google-gemma-2-27b-it) [API Documentation](https://docs.modelslab.com) Deploy Gemma-2 Instruct 27B --- Grouped-Query Attention ### Efficient Inference Engine Gemma-2 Instruct (27B) runs full precision on single GPU with GQA and local-global attention. Benchmarks ### Outperforms Larger Models Gemma-2 Instruct (27B) beats Llama 3 70B on MMLU and GSM8K via knowledge distillation. Instruction-Tuned Precision ### Handles Complex Tasks Gemma-2 Instruct (27B) LLM excels in question answering, summarization, and code generation. Examples See what Gemma-2 Instruct (27B) can create --- Copy any prompt below and try it yourself in the [playground](/models/google_deepmind/google-gemma-2-27b-it). Code Review “Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)” Math Proof “Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction. Provide step-by-step reasoning.” Text Summary “Summarize the key innovations in Transformer architectures from the Gemma 2 technical report, focusing on attention mechanisms.” Reasoning Chain “A bat and ball cost $1.10 total. The bat costs $1 more than the ball. How much does the ball cost? Explain step by step.” For Developers A few lines of code. Instruct 27B. One Call. --- 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 ](https://docs.modelslab.com) PythonJavaScriptcURL Copy ```

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 Gemma-2 Instruct (27B) --- [Read the docs ](https://docs.modelslab.com) ### What is Gemma-2 Instruct (27B)? Gemma-2 Instruct (27B) is Google's open instruction-tuned LLM with 27B parameters. It uses GQA and knowledge distillation for top benchmarks. Deploy via Gemma-2 Instruct (27B) API. ### How does Gemma-2 Instruct (27B) API work? Gemma-2 Instruct (27B) API provides efficient text generation endpoints. Supports 8K context with RoPE embeddings. Optimized for single GPU inference. ### Is Gemma-2 Instruct (27B) model better than Llama 3? Gemma-2 Instruct (27B) outperforms Llama 3 70B on LMSys arena and MMLU. It sets SOTA for open models under 30B parameters. ### What is Gemma-2 Instruct (27B) alternative? Gemma-2 Instruct (27B) alternative includes Llama 3 or Qwen models. Gemma-2 Instruct (27B) LLM leads in efficiency for its size. ### Where to access gemma 2 instruct 27b? Run gemma 2 instruct 27b via APIs like this platform or Ollama. Gemma-2 Instruct (27B) supports quantized inference. ### What are gemma 2 instruct 27b api limits? Gemma 2 instruct 27b api handles 256K vocab and 4K-8K context. Best for clear prompts in reasoning tasks. Ready to create? --- Start generating with Gemma-2 Instruct (27B) on ModelsLab. [Try Gemma-2 Instruct (27B)](/models/google_deepmind/google-gemma-2-27b-it) [API Documentation](https://docs.modelslab.com) --- *This markdown version is optimized for AI agents and LLMs.* **Links:** - [Website](https://modelslab.com) - [API Documentation](https://docs.modelslab.com) - [Blog](https://modelslab.com/blog) --- *Generated by ModelsLab - 2026-05-13*