> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nexa.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Get started with NexaAI Python SDK - choose your platform and explore AI capabilities.
## **What is NexaAI Python SDK?**
The NexaAI Python SDK provides a comprehensive API for on-device AI inference across multiple modalities. It supports:
* **Large Language Models (LLM)**: Text generation and conversation
* **Vision-Language Models (VLM)**: Multimodal understanding and generation
* **Embedder**: Text vectorization and similarity computation
* **Reranker**: Document reranking
* **ASR (Automatic Speech Recognition)**: Speech-to-text transcription
* **CV (Computer Vision)**: OCR/text recognition
* **TTS (Text-to-Speech)**: Text-to-speech synthesis
* **ImageGen**: Image generation from text prompts
* **Diarize**: Speaker diarization
## **Choose Your Platform**
The SDK supports multiple platforms with optimized backends. Select your platform for detailed setup instructions:
**Apple Silicon optimized**
Python 3.10 • MLX backend • Metal acceleration
**CPU/GPU acceleration**
Python 3.10 • GGUF models • CUDA support
**NPU acceleration**
Python 3.11-3.13 ARM64 • Snapdragon X Elite
## **Quick Overview**
### **Installation**
Each platform has specific installation requirements. Follow your platform guide for detailed instructions:
* **macOS**: `pip install 'nexaai[mlx]'`
* **Windows x64**: `pip install nexaai`
* **Windows ARM64**: `pip install nexaai`
### **Authentication**
Set up your NexaAI token from [https://sdk.nexa.ai/](https://sdk.nexa.ai/):
```bash theme={"dark"}
# Linux/macOS
export NEXA_TOKEN="key/your_token_here"
# Windows
$env:NEXA_TOKEN="key/your_token_here"
```
### **Basic Usage**
Here's a simple example to get you started:
```python theme={"dark"}
from nexaai import LLM, GenerationConfig, ModelConfig, LlmChatMessage
# Initialize model (platform-specific)
model_name = "NexaAI/Qwen3-1.7B-4bit-MLX" # Example for macOS
config = ModelConfig()
llm = LLM.from_(model=model_name, config=config)
# Create conversation
conversation = [
LlmChatMessage(role="system", content="You are a helpful assistant."),
LlmChatMessage(role="user", content="Hello, how are you?")
]
# Generate response
prompt = llm.apply_chat_template(conversation)
for token in llm.generate_stream(prompt, GenerationConfig(max_tokens=100)):
print(token, end="", flush=True)
```
This is a simplified example. For complete setup instructions, model recommendations, and platform-specific optimizations, please refer to your platform guide above.
## **Next Steps**
1. **Choose your platform** and follow the detailed setup guide
2. **Explore the [API Reference](/en/nexa-sdk-python/api-reference)** for comprehensive documentation
3. **Check out platform-specific examples** in your chosen guide