Nodegrove VRAM: can I run it?

Connect it to your AI

Claude Code

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

Claude, ChatGPT, Cursor and other apps

Add it as a custom connector with this URL (Streamable HTTP):

https://mcp.nodegrove.io/mcp

claude mcp add vram-mcp -- npx -y @nodegrove/vram-mcp@1.1.4

Cursor, VS Code and other apps (mcp.json)

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

Tools it offers

6 tools, as the server itself reported during our check on Oct 31, 2026.

• can_i_runCan 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: q
• what_fitsWhich 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
• estimate_vramHow 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 re
• estimate_from_hf_repoReads 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 weig
• list_modelsThe 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.
• list_gpusThe GPUs and machines nodegrove.io covers: memory, the memory a runtime can use and bandwidth, from the makers' specs, with each one's page.

Questions