Nodegrove VRAM: can I run it?

io.nodegrove/vram-mcp · v1.1.3

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

MCPremotelocal · npmno credentialsDeveloper toolsAI & LLM

Reported outcomes

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Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 6 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 100/100

What it does: 6 tools · 6 read, 0 write · Developer tools, AI & LLM

Test call list_models: worked (1,146 ms)

How the rating is made

Answers without login: +30
Speed of first answer: +10
Lists tools: +10
Tool descriptions: +15
Parameter descriptions: +10
Read/write annotations: +5
Safe test call: +20
More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL

https://mcp.nodegrove.io/mcp

Package

registry:   npm
identifier: @nodegrove/vram-mcp
version:    1.1.3
transport:  stdio

Tools

6 tools listed at last check

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 o
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
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).
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
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.
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.

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