---
title: Meta Llama 3 8B Instruct — Fast LLM | ModelsLab
description: Generate text and code with Meta Llama 3 8B Instruct. Fast inference, 8K context, instruction-tuned for dialogue. Try the API now.
url: https://modelslab.com/meta-llama-3-8b-instruct-reference
canonical: https://modelslab.com/meta-llama-3-8b-instruct-reference
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:01:39.891011Z
---

Available now on ModelsLab · Language Model

Meta Llama 3 8B Instruct Reference
Efficient reasoning. Production-ready.
---

[Try Meta Llama 3 8B Instruct Reference](/models/open_router/meta-llama-Llama-3-8b-chat-hf) [API Documentation](https://docs.modelslab.com)

Compact Power. Enterprise Scale.
---

Instruction-Tuned

### Dialogue Optimized Performance

Fine-tuned for conversation with supervised learning and human feedback alignment.

Fast Inference

### Grouped Query Attention

GQA architecture accelerates token generation without sacrificing output quality.

Extended Context

### 8K Token Window

Handle longer conversations and complex multi-turn interactions seamlessly.

Examples

See what Meta Llama 3 8B Instruct Reference can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/meta-llama-Llama-3-8b-chat-hf).

Code Documentation

“Write comprehensive API documentation for a Python function that validates email addresses using regex patterns. Include parameter descriptions, return types, and usage examples.”

Technical Explanation

“Explain how transformer attention mechanisms work in large language models. Use analogies to make it accessible to someone new to machine learning.”

Data Analysis

“Generate Python code to load a CSV file, calculate summary statistics, and create visualizations for sales data across regions.”

Problem Solving

“Provide step-by-step solutions to optimize database queries for a web application handling millions of daily requests.”

For Developers

A few lines of code.
Text and code. Eight billion parameters.
---

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

```
<code>import requests

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

FAQ

Common questions about Meta Llama 3 8B Instruct Reference
---

[Read the docs ](https://docs.modelslab.com)

### What makes Meta Llama 3 8B Instruct different from other open-source LLMs?

### Can I use Meta Llama 3 8B Instruct for code generation?

### How was Meta Llama 3 8B Instruct trained?

### Is Meta Llama 3 8B Instruct open-source?

### What's the knowledge cutoff date?

Ready to create?
---

Start generating with Meta Llama 3 8B Instruct Reference on ModelsLab.

[Try Meta Llama 3 8B Instruct Reference](/models/open_router/meta-llama-Llama-3-8b-chat-hf) [API Documentation](https://docs.modelslab.com)

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*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)

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*Generated by ModelsLab - 2026-04-15*