# KV Cache Optimizer AI Agent Connect

> Deterministic calculator for LLM KV cache memory, hardware utilization, and performance impact.

## Overview
- **Category:** hardware
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_J70L5rbBnbyXhpQy32OdlJSzj1qqHvxVEIyO0NlM/ai-agent-connect
- **Tags:** kv-cache, llm-inference, gpu-memory, paged-attention, throughput

## Description

This MCP server provides precise tools for estimating Large Language Model (LLM) KV cache memory consumption and hardware requirements. It allows users to calculate the exact memory footprint using `calculate_kv_cache_footprint`, evaluate memory savings with `analyze_optimization_strategy` (supporting sliding window and paged attention), and verify hardware compatibility via `evaluate_hardware_feasibility`. Additionally, users can determine the maximum efficient workload using `optimize_batch_configuration` to maximize throughput within GPU memory constraints.

## Tools

### analyze_optimization_strategy
Evaluates how much memory is saved or how efficiently memory is used when applying specific optimization techniques

### calculate_kv_cache_footprint
Determines the total memory required to store the KV cache for a specific model configuration and batch

### evaluate_hardware_feasibility
Checks if the requested workload fits within the physical constraints of the target GPU

### optimize_batch_configuration
Finds the highest possible batch size that does not violate the memory constraints

## 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 FP16.
```

**Response:** 
```
The total KV cache footprint for this configuration is 128 GiB.
```

**Prompt:** 
```
What is the optimal batch size for a model with 32 layers, 32 heads, 128 head dimension, 2048 sequence length, 40GB of GPU memory, FP16 precision, and 0.5 seconds latency?
```

**Response:** 
```
The optimal batch size is 2, providing a throughput impact of 4.0 tokens per second.
```

**Prompt:** 
```
Check if a 100GB KV cache fits in a GPU with 80GB of VRAM.
```

**Response:** 
```
The workload is not feasible as the requested cache size exceeds the available GPU memory.
```

## Frequently Asked Questions

**How does this tool help with LLM inference optimization?**
It provides deterministic calculations for KV cache size, memory bandwidth requirements, and optimal batch sizes, helping you avoid Out-of-Memory errors.

**Can I calculate the impact of quantization?**
Yes, by using `calculate_kv_cache_footprint` with the INT8 precision type, you can see the 50% memory reduction compared to FP16.

**Does it support Paged Attention analysis?**
Yes, the `analyze_optimization_strategy` tool can calculate the cache hit rate for paged attention based on available pages.