# KV Cache Memory Optimizer AI Agent Connect

> Deterministic calculator for LLM KV cache memory, throughput, and optimization analysis.

## Overview
- **Category:** optimization
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_5QTkZtmGwTPf4KDlzyFNQsxJGyHjfaJ6fYBaUPtc/ai-agent-connect
- **Tags:** kv-cache, llm, gpu, memory-optimization, inference

## Description

This MCP server provides precise mathematical modeling for LLM inference memory management. It allows AI agents to calculate the exact memory footprint of Key-Value (KV) caches, evaluate the efficiency of different strategies like `sliding_window` or `paged_attention`, and predict performance impacts. Use `calculate_kv_cache_footprint` to determine raw memory requirements, `analyze_optimization_strategies` to compare cache management techniques, and `estimate_inference_performance` to find the optimal batch size for a given GPU capacity.

## Tools

### analyze_optimization_strategies
Evaluates the memory reduction and efficiency gains provided by different cache management techniques

### calculate_kv_cache_footprint
Determines the raw memory required for the KV cache based on specific architectural and operational parameters

### estimate_inference_performance
Predicts the impact of batching and memory bandwidth on the actual speed of token generation

## Prompt Examples

**Prompt:** 
```
Calculate the KV cache footprint for a model with 32 layers, 32 heads, 128 head dimension, 2048 sequence length, and a batch size of 8 using 40GB of GPU memory.
```

**Response:** 
```
The total KV cache size for this configuration is 134,217,728 bytes (128 MB).
```

**Prompt:** 
```
What is the optimal batch size for a 24GB GPU if my current KV cache is 4GB?
```

**Response:** 
```
The optimal batch size is 4, ensuring the cache remains under the 80% safety threshold of 19.2 GB.
```

**Prompt:** 
```
Compare the memory reduction of using a sliding window of 512 tokens for a 2048 token sequence.
```

**Response:** 
```
Using a sliding window of 512 tokens provides a cache reduction ratio of 0.25 compared to a full cache.
```

## Frequently Asked Questions

**How does this tool help with LLM deployment?**
It helps by providing deterministic calculations for KV cache size and throughput, allowing you to prevent Out-of-Memory errors and find the best batch size for your hardware.

**Can I calculate the savings from INT8 quantization?**
Yes, the `calculate_kv_cache_footprint` tool provides the `quantizedKvSavingsBytes` which shows the memory saved when switching to INT8.

**What is the difference between the cache strategies?**
The tool supports `full_cache` for complete context, `sliding_window` for recent token retention, and `paged_attention` for efficient non-contiguous memory allocation.