All servers · AI models & agents · Code & repositories
Nodegrove VRAM: can I run it?
io.nodegrove/vram-mcp · v1.1.4
New (7 days)
Measured by MCP Station
| Last check | Reachable |
|---|---|
| Checked at | — |
| Check success 7 / 30 days | — |
| First-try success 30 days | — |
| Median response | — |
| Lookups 30 days | — |
| Authentication | No auth |
| MCP version | 2025-06-18 |
| Rank score | — |
| Declared risk | Read-only (all tools declare readOnlyHint) |
| Capabilities declared | tools |
| Tool definitions | 6 tools · with description 100% · input schema is an object 100% · read/destructive hints 100% |
Description written by the publisher, not verified
Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.
Connect
streamable-http · https://mcp.nodegrove.io/mcp
claude mcp add --transport http vram-mcp https://mcp.nodegrove.io/mcp{
"mcpServers": {
"vram-mcp": {
"url": "https://mcp.nodegrove.io/mcp"
}
}
}Tools as reported by the server
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: quantisation, KV cache, context, another card or a smaller model. Model: a name owhat_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 largest that fits, the best at Q8 and the first out of reach. GPU: a name or idestimate_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 repo id, or its architecture (params_b, layers, kv_heads, head_dim).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 weights + KV cache + overhead at every quantisation. For models nodegrove.io has notlist_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.
Repository · Website · JSON · Text for agents · Registry updated 2026-10-19