> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sunbird.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

# 🌻 Sunflower Quantized Inference

The **Sunflower** models are available in **14B** and **32B** sizes, supporting **8-bit** and **4-bit quantized inference**. These models are capable of high-quality **translation**, text generation, and conversational tasks, optimized for efficient performance on GPUs with limited memory.

<CardGroup cols={2}>
  <Card title="8-bit Quantization" icon="memory">
    Balanced memory usage (\~16GB for 14B) with high accuracy.
  </Card>

  <Card title="4-bit Quantization" icon="bolt">
    Low memory usage (\~10GB for 14B) and faster inference speeds.
  </Card>
</CardGroup>

<Warning>
  Do not set both 8-bit and 4-bit modes simultaneously.
</Warning>

## Model Variants

### Sunflower 14B

* **14B 8-bit:** Balanced memory and accuracy, suitable for most GPUs.
* **14B 4-bit:** Optimized for memory-limited GPUs and faster inference.

### Sunflower 32B

* **32B 8-bit:** High accuracy, requires significant VRAM.
* **32B 4-bit:** Reduced memory usage, slightly lower accuracy but faster.

## Best Practices

<Tip>
  Use **4-bit models** when GPU memory is constrained. Use **8-bit models** when you need maximum accuracy and have sufficient VRAM.
</Tip>

* Monitor GPU memory for large inputs or batch processing.
* Adjust inference parameters (sequence length) to fit your hardware limits.