MCP server

Nodegrove VRAM: can I run it? Official

io.nodegrove/vram-mcp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

nodegrove.io (verified by the MCP Registry)

Status

Working at last check

Tools

6

Token cost

About 3,039 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

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

Gemini CLI

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

Cursor

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

VS Code

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

Antigravity

agy mcp add vram-mcp https://mcp.nodegrove.io/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"vram-mcp","server_url":"https://mcp.nodegrove.io/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"vram-mcp","server_url":"https://mcp.nodegrove.io/mcp"}

Tools

• can_i_run ~1127 tokens
  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 o
• estimate_from_hf_repo ~346 tokens
  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
• estimate_vram ~906 tokens
  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).
• list_gpus ~112 tokens
  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.
• list_models ~130 tokens
  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_fits ~418 tokens
  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

Browse

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