MCP For Claude Marketplace
KV Cache Memory Optimizer

KV Cache Memory Optimizer MCP Connector for Claude

A+

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

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

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.

kv-cachellmgpumemory-optimizationinference

3 tools expose this connector's capabilities to your AI agent.

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

See how to talk to your AI agent using KV Cache Memory Optimizer.

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.

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

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

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

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

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

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.

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