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Connect Kv Cache Memory Optimizer to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Model exact memory footprints and optimize inference performance.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through KV Cache Optimizer.

  1. 01

    Analyze optimization strategies

    This capability evaluates how different cache management techniques reduce memory usage and improve efficiency.

  2. 02

    Calculate kv cache footprint

    This capability determines the exact amount of raw memory required for a KV cache based on your specific model architecture and operational settings.

  3. 03

    Estimate inference performance

    This capability predicts how batch sizes and memory bandwidth will impact the actual speed of your token generation.

Real work

What teams actually run

01

Hardware Capacity Planning

Determine if a specific model configuration will fit on your available GPU before deployment.

02

Inference Speed Tuning

Find the best batch size to maximize throughput without hitting memory limits.

03

Algorithm Testing

Compare the memory savings of sliding window attention against standard cache methods.

04

Cost Optimization

Model how different settings impact the efficiency of your inference hardware.

One connector, every AI

KV Cache Optimizer works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put KV Cache Optimizer to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

860ms average. Fast in production.

KV Cache Optimizer is checked daily against the live service.

Daily averagePeak 972ms
Oct 3Today
Fastest day
699ms
Slowest day
972ms
14-day trend
Stable-4%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of KV Cache Optimizer, so you can see the experience inside your AI.

It does not authenticate your account with KV Cache Optimizer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

KV Cache Optimizer Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_5QTkZtmGwTPf4KDlzyFNQsxJGyHjfaJ6fYBaUPtc/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — KV Cache Optimizer capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "kv-cache-memory-optimizer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_5QTkZtmGwTPf4KDlzyFNQsxJGyHjfaJ6fYBaUPtc/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up KV Cache Optimizer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work KV Cache Optimizer owners hand off.

This MCP is built for engineers working on LLM deployment and inference optimization.

  • 01

    ML Engineers

    They hand off hardware constraint modeling and memory math to the agent.

  • 02

    AI Infrastructure Engineers

    They use the capability to plan GPU allocation and batching strategies.

  • 03

    LLM Researchers

    They use it to test the theoretical memory impact of new architectural parameters.

FAQ

Questions KV Cache Optimizer owners ask.

  • 01

    What can this MCP calculate?

    It calculates KV cache footprints, compares cache management strategies, and estimates inference performance based on batching and bandwidth.

  • 02

    How does it help with GPU management?

    It helps you find the optimal batch size for your specific GPU capacity and ensures your cache stays within safe memory thresholds.

  • 03

    Can I use this to compare different attention mechanisms?

    Yes, you can use the optimization strategy capability to compare the memory reduction provided by techniques like sliding windows.

  • 04

    Which AI clients can use this MCP?

    You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code.

  • 05

    Does this MCP require me to host anything?

    No, Vinkius hosts and manages the MCP for you, so it is ready to use immediately after connection.

  • 06

    How does this capability 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.

  • 07

    Can I calculate the savings from INT8 quantization?

    Yes, the calculate_kv_cache_footprint capability provides the quantizedKvSavingsBytes which shows the memory saved when switching to INT8.

  • 08

    What is the difference between the cache strategies?

    The capability supports full_cache for complete context, sliding_window for recent token retention, and paged_attention for efficient non-contiguous memory allocation.

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