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Connect AI Quantization Cost Benefit Engine to your AI

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. Find the perfect balance between speed, cost, and accuracy.

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 · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Quantization Engine.

  1. 01

    Check hardware compatibility

    Use this capability to validate if a specific quantization precision level can actually run on your target hardware.

  2. 02

    Evaluate accuracy impact

    Run this to determine if the expected loss in model accuracy is acceptable for your specific deployment target.

  3. 03

    Find optimal precision

    This capability identifies the best precision level, like INT8 or FP16, by balancing the cost, speed, and accuracy requirements.

  4. 04

    Get deployment savings

    Calculate the total monetary savings achieved by switching from your baseline model to a quantized version.

Real work

What teams actually run

01

Edge Device Deployment

You need to run a large model on a low-power device. Use this MCP to find the highest precision that still maintains acceptable accuracy.

02

Cloud Cost Reduction

Your current model is too expensive to run 24/7. Use this MCP to calculate how much money you save by quantizing the model.

03

Performance Benchmarking

You need to compare three different model versions. Use this MCP to determine which one offers the best speed-to-accuracy ratio.

04

New Hardware Integration

Your team is moving to a new accelerator card. Use this MCP to verify that the model's quantization level is compatible with the new hardware.

One connector, every AI

AI Quantization Engine 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 AI Quantization Engine 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

914ms average. Fast in production.

AI Quantization Engine is checked daily against the live service.

Daily averagePeak 1033ms
Oct 3Today
Fastest day
663ms
Slowest day
1033ms
14-day trend
Improving-7%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 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 AI Quantization Engine, so you can see the experience inside your AI.

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

AI Quantization Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_WMXF33rxvYjhT36OGw4KlQyAVwy0p3eYpyfyAFSL/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 — AI Quantization Engine capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-quantization-cost-benefit-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_WMXF33rxvYjhT36OGw4KlQyAVwy0p3eYpyfyAFSL/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 AI Quantization Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Quantization Engine owners hand off.

This MCP is built for technical teams making deployment decisions. If you're an ML Engineer or AI Architect tasked with moving a model from research to production, this capability gives you the data you need to justify your choices. It handles the complex math of cost vs. performance.

  • 01

    ML Engineer

    Uses this to test different quantization levels and ensure the model meets latency targets.

  • 02

    AI Architect

    Uses this to model the total cost of ownership (TCO) for cloud versus edge deployments.

  • 03

    DevOps Engineer

    Uses this to validate that the optimized model will run correctly on the target production hardware.

FAQ

Questions AI Quantization Engine owners ask.

  • 01

    What exactly is model quantization?

    Quantization is a technique that reduces the number of bits used to represent the model's weights. This shrinks the model size and speeds up inference, but it can sometimes reduce accuracy.

  • 02

    Does this MCP handle all types of hardware?

    No. You must use the check_hardware_compatibility capability to validate if the specific quantization precision level you choose can actually run on your intended hardware.

  • 03

    Can I use this for both cloud and edge deployments?

    Yes. The engine is designed to evaluate the economics and performance impact for both large-scale cloud deployments and resource-constrained edge devices.

  • 04

    What inputs does the get_deployment_savings capability need?

    This capability requires baseline model metrics and the proposed quantized model metrics. It calculates the total monetary savings achieved by making the switch.

  • 05

    Is this MCP only for specific model types?

    The MCP is a decision-support engine that evaluates the process of quantization, making it applicable to various model architectures and deployment scenarios.

  • 06

    How can I calculate my monthly savings?

    You can use the get_deployment_savings capability by providing your current monthly cost, the expected model size reduction, and the projected inference speedup.

  • 07

    Can I check if INT4 is supported on mobile devices?

    Yes, use the check_hardware_compatibility capability with the precision level set to 'int4' and the hardware type set to your target device (e.g., 'Edge-Mobile').

  • 08

    How do I find the best balance between speed and accuracy?

    The find_optimal_precision capability is designed for this. It takes your target latency and accuracy budget into account to recommend the best precision level for your hardware.

Explore

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