Nodegrove VRAM: can I run it? MCP Server

by nodegrove Your server? Claim it

answering

Nodegrove VRAM: can I run it? is answering right now. Last checked 13 min ago. 262 installs a week from npm. It exposes 6 tools. Last commit 16 Oct 2026.

Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.

Installs per day peak 316 · avg 24 · +378% w/w

a month agotoday

Uptime history 13 days of history · worst day 98%

13 days agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

141 ms

Response time

average over 24h

262

Installs / week

npm and PyPI

What changed 6

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 6 October 2026. No other catalogue keeps this.

16 Oct a tool changed version

9 Oct a tool description was rewritten list_models

9 Oct a tool changed version

6 Oct a tool description was rewritten list_models

6 Oct a tool changed version

5 Oct a tool changed version

What the code does

We read the source, 6 d ago · tools taken from the live server · rules ccca72095570

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

Runs an external command llm-math/build.mjs:19

execFileSync(process.execPath, [tsc, '-p', 'tsconfig.build.json'], { stdio: 'inherit' });

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 13 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

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

~/Library/Application Support/Claude/claude_desktop_config.json

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

~/.codex/config.toml

[mcp_servers.vram-mcp]
url = "https://mcp.nodegrove.io/mcp"

.cursor/mcp.json

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

.vscode/mcp.json

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

Available tools 6

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

estimate

estimate_from_hf_repo

Reads any Hugging Face model repo's config.json and parameter count and estimates its memory: the attention layout found (standard, sliding-window, hybrid or latent), how much each 1,000 tokens of context costs, and weights + KV cache + overhead at every quantisation. For models nodegrove.io has not reviewed; anything the reader cannot model is listed in warnings.

estimate_vram

How much memory an LLM needs: weights + KV cache + overhead at each quantisation (or one), at a given context, and the smallest common card class that holds each. Model: a name or id from list_models, any Hugging Face repo id, or its architecture (params_b, layers, kv_heads, head_dim).

can

can_i_run

Can this GPU run this open-weight LLM? Returns fits, tight or no, the memory split (weights, KV cache, overhead), a decode-speed ceiling, the longest context that fits and, on a no, every change that would make it fit: quantisation, KV cache, context, another card or a smaller model. Model: a name or id from list_models, any Hugging Face repo id, or its architecture. GPU: a name or id from list_gpus, or vram_gb for any other card.

gpus

list_gpus

The GPUs and machines nodegrove.io covers: memory, the memory a runtime can use and bandwidth, from the makers' specs, with each one's page.

models

list_models

The open-weight LLMs nodegrove.io has verified against their config.json (data version 2026-10-16): id, size, attention design, native context, licence, memory at Q4 with 8k context and each model's page.

what

what_fits

Which open-weight LLMs fit this GPU: every model in list_models checked at one quantisation and context, with a recommended everyday model (the biggest class that fits with room for context at conversational speed), the largest that fits, the best at Q8 and the first out of reach. GPU: a name or id from list_gpus, or vram_gb for any other card.

Endpoints

URL	Transport	State	Latency	Checked
https://mcp.nodegrove.io/mcp	streamable-http	answering	155 ms	13 min ago

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Causari

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Records the prompt, model and files an agent declared for a line. It does not prove who typed it.

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Paste a draft and see the habits that make writing read like it came from an AI model.

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ModelsLab

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Generate images, video, speech and music, and run LLM chat, across 10,000+ models on ModelsLab.

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Nodegrove VRAM: can I run it? — questions

Answers built from our own checks of this server.

What can Nodegrove VRAM: can I run it? do?

It exposes 6 tools, read directly from the server on our last check. Among them: can_i_run, estimate_from_hf_repo, estimate_vram, list_gpus, list_models, what_fits. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

Is Nodegrove VRAM: can I run it? working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 141 ms. The bar chart above shows every period we have measured.

How do I connect Nodegrove VRAM: can I run it??

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Nodegrove VRAM: can I run it? need an API key?

No. Nodegrove VRAM: can I run it? completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 6 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Nodegrove VRAM: can I run it??

It answers our handshake in 141 ms on average, which is faster than 77% of all working MCP servers we measure. That puts it in the quick quarter of the ecosystem. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use Nodegrove VRAM: can I run it??

The npm package @nodegrove/vram-mcp was installed 262 times in the last week. Week over week that is +378%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is Nodegrove VRAM: can I run it? open source?

Yes — it is published under the MIT licence, written in TypeScript and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.