Can I run it? in your AI assistant

Ask your assistant whether a model runs on your card and it answers with the arithmetic behind every figure on this site: the same formulas as the checker, over the same 29 models and 20 GPUs, and over any other model on Hugging Face, read from its config.json. It is free, read-only and needs no account or key.

Connect

Remote, with nothing to install. Add this URL to any client that speaks Streamable HTTP:

https://mcp.nodegrove.io/mcp

claude mcp add --transport http nodegrove-vram https://mcp.nodegrove.io/mcp

{
  "mcpServers": {
    "nodegrove-vram": {
      "url": "https://mcp.nodegrove.io/mcp"
    }
  }
}

{
  "servers": {
    "nodegrove-vram": {
      "type": "http",
      "url": "https://mcp.nodegrove.io/mcp"
    }
  }
}

devin mcp add -s user nodegrove-vram https://mcp.nodegrove.io/mcp

{
  "mcpServers": {
    "nodegrove-vram": {
      "url": "https://mcp.nodegrove.io/mcp"
    }
  }
}

{
  "mcpServers": {
    "nodegrove-vram": {
      "type": "streamableHttp",
      "url": "https://mcp.nodegrove.io/mcp"
    }
  }
}

codex mcp add nodegrove-vram --url https://mcp.nodegrove.io/mcp

gemini mcp add -s user --transport http nodegrove-vram https://mcp.nodegrove.io/mcp

{
  "context_servers": {
    "nodegrove-vram": {
      "url": "https://mcp.nodegrove.io/mcp"
    }
  }
}

Or run it on your own machine over stdio. It needs Node.js 20 or newer and nothing else:

{
  "mcpServers": {
    "nodegrove-vram": {
      "command": "npx",
      "args": [
        "-y",
        "@nodegrove/vram-mcp"
      ]
    }
  }
}

What you can ask

Six tools. You ask in your own words; the assistant picks the tool. Names are matched the way people type them, so "4090", "M4 Max" and "Llama-3.3-70B-Instruct" all work, and a card that is not listed can be described by its memory.

An answer

Asked whether an RTX 4090 runs Llama 3.3 70B, the server answers:

No: Llama 3.3 70B at Q4_K_M with 8,192 tokens of context needs 45.8 GB, and the RTX 4090 holds 22.8 GB after headroom. Short by 23 GB.

What would work instead:

• No context length helps: the weights alone are 43.1 GB before a single token of conversation.
• RTX 6000 Ada (48 GB) is within a whisker: 45.8 GB against 45.6 GB after headroom. With the KV cache at Q8 it needs 44.4 GB and fits.
• The smallest card here that runs it exactly as asked: A100, 80 GB usable.
• The biggest model your card does run at these settings, counting mixture-of-experts models at their dense equivalent: Qwen3 32B, 22.4 GB at ~37 tokens/s.

Alongside the sentences come the numbers as fields (memory split, budget, speed ceiling, longest context), links to the model and card pages here, a link that opens the checker on the same settings, and the assumptions behind each figure. When a model does not fit, the answer also says which GPU size a Nodegrove workspace would attach for it; Nodegrove is in early access.

How it works

The server is this site's arithmetic, published as the open-source package @nodegrove/llm-math. The pages, the calculators, the open dataset and the server all import it, so they cannot disagree, and a correction reaches all of them at once.

• Memory is weights plus KV cache plus overhead, with each model's cache counted the way it actually caches: sliding-window, hybrid and latent attention store far less than a standard transformer. The method has every constant.
• Models in the list were read from their config.json and checked against Hugging Face; the data version is 2026-09-25. Any other repo is read live by the same rules, and anything the reader cannot model is named in the answer rather than guessed.
• Speed is a ceiling from memory bandwidth, not a benchmark. The faster the figure, the further real runtimes fall below it, so figures of 100 tokens/s or more say "at most".

Limits

• Every figure is an estimate from stated formulas. Real usage moves with the runtime, batch size, flash attention and cache quantisation: read "fits" as "worth trying".
• For a mixture-of-experts model read from Hugging Face, the speed needs the active parameters from its model card; without them the answer gives memory only.
• The remote endpoint answers up to 120 requests a minute from one address. A Hugging Face lookup waits up to 8 seconds.

Privacy

• No account, no key, no cookies. The remote server runs on Cloudflare Workers. We keep no request logs and no record of what you ask: every request is answered by a fresh instance and forgotten. Cloudflare keeps standard edge logs for a short period, as for any website.
• The rate limit counts requests per address for one minute, inside Cloudflare, and keeps nothing after that.
• When you ask about a Hugging Face repo, the server fetches that repo's public config.json and metadata. Only the repo name is sent.
• The local version sends nothing anywhere, except to huggingface.co when you ask about a repo there.

The privacy notice has the same in full.

Open source

The server and the package are on GitHub at nodegrove/vram-mcp, the server is on npm as @nodegrove/vram-mcp, and it is listed in the official MCP Registry as io.nodegrove/vram-mcp. The code is MIT; the model and GPU data is CC BY 4.0, the same licence as the dataset. To use the math in your own code:

npm install @nodegrove/llm-math

Found an answer that disagrees with its source? Open an issue or write to info@nodegrove.io.

• Protocol: the Model Context Protocol, served to clients on the 2025 revisions and on 2026-07-28.
• Model architecture: each model's config.json on Hugging Face, linked from every answer and every model page.
• GPU memory and bandwidth: the manufacturers' specifications, linked from every GPU page.