# Quantization Impact Calculator AI Agent Connect

> Simulate and quantify the trade-offs between model compression and performance.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_lE4kL3fM0KpmqqGlMSUHmyjXY7KXOrC6lU9rOKgx/ai-agent-connect
- **Tags:** quantization, llm, performance, compression, inference

## Description

This MCP server provides deterministic tools to analyze how quantization affects AI model performance. Use `calculate_quantization_metrics` to predict quality degradation, memory reduction, and latency improvements for different compression levels like INT8 or AWQ. It helps engineers determine if a compressed model meets their specific task requirements, such as generation or extraction, by calculating throughput increases and checking against quality thresholds.

## Tools

### evaluate_task_sensitivity
Determine the multiplier applied to quality degradation based on the complexity of the task

### get_quantization_presets
Retrieve standard degradation ranges and memory ratios for supported quantization methods

### calculate_quantization_metrics
Calculate the primary technical impacts (quality, memory, and latency) for a specific quantization configuration

## Prompt Examples

**Prompt:** 
```
Calculate the impact of using INT8 quantization on a model with 80 quality score, 100ms latency, and 16GB memory for a classification task.
```

**Response:** 
```
The INT8 quantization results in a quality score of 77.6, a 2.4% degradation, 4x memory reduction, and a 1.35x latency improvement.
```

**Prompt:** 
```
What is the memory reduction factor for AWQ?
```

**Response:** 
```
The memory reduction factor for AWQ is 4x.
```

**Prompt:** 
```
How sensitive is a generation task to quantization?
```

**Response:** 
```
Generation tasks are classified as high sensitivity, meaning they receive a higher multiplier for quality degradation.
```

## Frequently Asked Questions

**How do I calculate the impact of INT4 quantization?**
You can use the `calculate_quantization_metrics` tool. Provide your base model's quality score, latency, and memory, then specify 'INT4' as the quantization level.

**Does task type affect the results?**
Yes. Tasks like 'generation' or 'extraction' are more sensitive to precision loss. You can use `evaluate_task_sensitivity` to see how different task types scale degradation.

**What are the supported quantization levels?**
The server supports FP16, INT8, INT4, GPTQ, and AWQ. You can retrieve specific details for each using `get_quantization_presets`.