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Connect GPU Inference Memory Calculator 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. Stop guessing your VRAM requirements and optimize your hardware.

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 GPU Inference Memory Calculator.

  1. 01

    Estimate total vram

    This capability calculates the total VRAM needed to run a specific batch size. It accounts for both weights and cache overhead.

  2. 02

    Calculate max batch capacity

    This capability finds the largest batch size your specific GPU can handle. It works within your defined memory limits to prevent crashes.

  3. 03

    Get kv cache per request

    This capability determines the memory footprint of the Key-Value cache for a single inference request. It uses model architecture details like layers and heads.

  4. 04

    Get model weights size

    This capability calculates the VRAM required just to load model parameters. It supports various precisions including FP32, FP16, BF16, INT8, and INT4.

Real work

What teams actually run

01

Hardware Budgeting

Determine if a single A100 or H100 can handle your target model and batch size.

02

Quantization Testing

Compare the memory savings when switching from FP16 to INT4 weights.

03

Scaling Inference

Calculate how many users can be served simultaneously on a specific GPU setup.

04

Architecture Analysis

Estimate how context length increases will impact your total VRAM usage.

One connector, every AI

GPU Inference Memory Calculator 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 GPU Inference Memory Calculator 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

708ms average. Fast in production.

GPU Inference Memory Calculator is checked daily against the live service.

Daily averagePeak 884ms
Oct 3Today
Fastest day
559ms
Slowest day
884ms
14-day trend
Slowing+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 GPU Inference Memory Calculator, so you can see the experience inside your AI.

It does not authenticate your account with GPU Inference Memory Calculator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

GPU Inference Memory Calculator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_Po32HLoFfJyACkPs6caCBt7wRipx1gN5av2RafWO/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 — GPU Inference Memory Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "gpu-inference-memory-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Po32HLoFfJyACkPs6caCBt7wRipx1gN5av2RafWO/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 GPU Inference Memory Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work GPU Inference Memory Calculator owners hand off.

This MCP is built for engineers and researchers managing LLM deployment and hardware allocation.

  • 01

    ML Engineers

    They use it to verify if a model fits on a specific GPU before deployment.

  • 02

    Infrastructure Architects

    They use it to plan hardware clusters and batch capacity for serving models.

  • 03

    AI Researchers

    They use it to estimate the memory costs of different model architectures and precisions.

FAQ

Questions GPU Inference Memory Calculator owners ask.

  • 01

    What precisions does this MCP support?

    It supports FP32, FP16, BF16, INT8, and INT4 for weight size calculations.

  • 02

    How does it calculate KV cache size?

    It uses model architecture details like the number of layers, number of heads, head dimension, and context length.

  • 03

    Can I use this to prevent OOM errors?

    Yes, you can use the batch capacity capability to find the maximum number of requests that fit within your specific VRAM limit.

  • 04

    Does it account for both weights and cache?

    Yes, the total VRAM estimation capability combines model weight memory and KV cache overhead.

  • 05

    Which AI clients can use this MCP?

    You can connect it to any MCP-compatible client like Claude, Cursor, or Windsurf.

  • 06

    What precision modes are supported?

    The calculator supports FP32, FP16, BF16, INT8, and INT4 precision modes.

  • 07

    How do I calculate the total VRAM for a batch?

    You can use the estimate_total_vram capability by providing the pre-calculated weight memory, KV cache size per request, and your desired batch size.

  • 08

    Can I determine the maximum number of concurrent requests for my GPU?

    Yes, use calculate_max_batch_capacity by providing your total VRAM budget, model weight memory, and KV cache size per request.

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