Nodegrove VRAM: can I run it?

Name: Nodegrove VRAM: can I run it?
Author: io.nodegrove

MCP serverMCP

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

by io.nodegroverepository ↗homepage ↗v1.1.1(MIT AND CC-BY-4.0)

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Evidence

Everything here was measured by our prober or read from a registry. Nothing is self-reported.

30-day availability17/17 checks ok

when	check	result	http	latency	detail
34 min ago	mcp initialize	ok	200	39 ms	server nodegrove-vram 1.1.2 · protocol 2025-06-18 · 6 tools
8 h ago	mcp initialize	ok	200	40 ms	server nodegrove-vram 1.1.2 · protocol 2025-06-18 · 6 tools
15 h ago	mcp initialize	ok	200	35 ms	server nodegrove-vram 1.1.2 · protocol 2025-06-18 · 6 tools
22 h ago	mcp initialize	ok	200	38 ms	server nodegrove-vram 1.1.2 · protocol 2025-06-18 · 6 tools
29 h ago	mcp initialize	ok	200	38 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools
2 d ago	mcp initialize	ok	200	94 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools
2 d ago	mcp initialize	ok	200	58 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools
2 d ago	mcp initialize	ok	200	45 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools
2 d ago	mcp initialize	ok	200	82 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools
3 d ago	mcp initialize	ok	200	54 ms	server nodegrove-vram 1.1.1 · protocol 2025-06-18 · 6 tools

Tools observed via MCP handshake

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 th
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 th
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 eac
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 la
list_models — The open-weight LLMs nodegrove.io has verified against their config.json (data version 2026-10-09): id, size, attention design, native context, licence, memory
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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No long description beyond the summary.

mcpmodel-context-protocolmcp-serverllmvramgpulocal-llmhugging-facekv-cache

kind Official MCP Registry

name Official MCP Registry

license npm registry

repoUrl Official MCP Registry

summary Official MCP Registry

version Official MCP Registry

description Official MCP Registry

homepageUrl Official MCP Registry

publisherName Official MCP Registry

Provenance

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npm registry@nodegrove/vram-mcp

first seen 4 d ago · fetched 3 h agoopen ↗

Official MCP Registryio.nodegrove/vram-mcp

first seen 4 d ago · fetched 33 h agoopen ↗

Field history (11 recent events)

4 h ago · observed · packageFacts → {"name":"@nodegrove/vram-mcp","registry":"npm","publishedAt":"2026-10-09T00:35:29.554Z","latestVersion":"1.1.2","weeklyD
33 h ago · declared · version → "1.1.1"
4 d ago · declared · license → "(MIT AND CC-BY-4.0)"
4 d ago · observed · packageFacts → {"name":"@nodegrove/vram-mcp","registry":"npm","publishedAt":"2026-10-05T22:16:18.423Z","latestVersion":"1.1.1","weeklyD
4 d ago · declared · repoUrl → "https://github.com/nodegrove/vram-mcp"
4 d ago · declared · summary → "Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU."
4 d ago · declared · protocols → ["mcp"]
4 d ago · declared · version → "1.0.0"
4 d ago · declared · description → "Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU."
4 d ago · declared · publisherName → "io.nodegrove"
4 d ago · declared · homepageUrl → "https://nodegrove.io/mcp"

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