MCP serverio.nodegrove/vram-mcp

Can this LLM run on my GPU?

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

UNRATEDActivestreamable-httpmcp.nodegrove.io

Overview

Score?

UNRATED 0.143

of what a free look can see, on 1 looks

Looks

last 10 hr ago

Tools

URL

mcp.nodegrove.io/mcp

streamable-http

Says it is

nodegrove-vram 1.0.0

protocol 2025-06-18

In the record since

10 hr ago

0median 0.606 · this server 0.143 · highest on record 0.8561

The tools this server lists, read out of the definition it returned
Tool	Schema
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	input · no output
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	input · no output
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	input · no output
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.	input · no output
list_models The open-weight LLMs nodegrove.io has verified against their config.json (data version 2026-09-25): id, size, attention design, native context, licence, memory at Q4 with 8k contex	input · no output
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 fo	input · no output

Verify it yourselfnpx teppi-check https://mcp.nodegrove.io/mcpcurl -s https://api.teppi.xyz/v1/trust/mcp/mcs_01M42QZS8NKKFRPBGYHXD1WBFW