Nodegrove VRAM: can I run it?
NPM · @NODEGROVE/VRAM-MCP · 2 COMPONENTS
Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.
Available components
Last scanned (20 hours ago)
Scores higher than 46% of npm servers
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 0 of 3 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency35
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: the license ((MIT AND CC-BY-4.0)) isn't a recognized OSI-approved license. See how to fix → Fail
- Actively maintained (last published 2 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability66
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1578 tokens (~263/item across 6 items; 6 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management63
- Stability observed for 19 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 22 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.
- 20 Oct 26 +16
- Malware scan: unverified → pass ▲ security
- 19 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- Package version: 1.1.3 → 1.1.4 functional
- 18 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 16 Oct 26 −14
- Malware scan: pass → unverified ▼ security
- Package version: 1.1.2 → 1.1.3 functional
- 13 Oct 26 +35
- Injection markers: unverified → pass ▲ security
- Tool coverage: unverified → 100 ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Stability: unverified → 0.33 ▲ functional
- 11 Oct 26 −30
- Tool safety: pass → unverified ▼ security
- Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet. security
- Capabilities: pass → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- First check of Schema quality: unverified functional
- Package version: 1.0.0 → 1.1.2 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 22 Oct 2026 · Analysed npm/@nodegrove/vram-mcp@1.1.4
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 3 packages
| Packages resolved | 3 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
can_i_run Can I run it? ~480
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 or id from list_models, any Hugging Face repo id, or its architecture. GPU: a name or id from list_gpus, or vram_gb for any other card.
| Name | Type | Req | Description |
|---|---|---|---|
| active_params_b | number | – | Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling. |
| apple_silicon | boolean | – | vram_gb is Apple unified memory; the GPU can use about 75% of it by default. |
| architecture | object | – | A model described by its config.json values instead of a name. |
| bandwidth_gb_s | number | – | Memory bandwidth from the maker's spec, GB/s, for a speed ceiling. |
| context | integer | – | Tokens held in context: prompt plus conversation. |
| gpu | string | – | A GPU from list_gpus (id or name, e.g. "rtx-4090", "4090" or "M4 Max"). |
| kv_cache | string | – | KV cache precision. fp16 is what most runtimes use; q8 halves the cache. |
| model | string | – | A model from list_models (id or name, e.g. "llama-3.3-70b" or "Llama 3.3 70B"), or any Hugging Face repo id (e.g. "Qwen/Qwen3-8B"), read live from its config.json. |
| quant | string | – | Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). q4 is the common default. |
| vram_gb | number | – | Memory of a card not in list_gpus, GB. For a Mac, its unified memory with apple_silicon: true. |
No output schema declared.
No examples provided.
estimate_from_hf_repo Estimate VRAM from a Hugging Face repo ~198
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 reviewed; anything the reader cannot model is listed in warnings.
| Name | Type | Req | Description |
|---|---|---|---|
| active_params_b | number | – | Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling. |
| context | integer | – | Tokens held in context: prompt plus conversation. |
| kv_cache | string | – | KV cache precision. fp16 is what most runtimes use; q8 halves the cache. |
| repo | string | yes | Hugging Face repo id, e.g. "Qwen/Qwen3-8B", or its huggingface.co URL. |
No output schema declared.
No examples provided.
estimate_vram Estimate VRAM ~312
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).
| Name | Type | Req | Description |
|---|---|---|---|
| active_params_b | number | – | Parameters read per token, billions, for a mixture-of-experts model read from Hugging Face (from its model card). Sets the speed ceiling. |
| architecture | object | – | A model described by its config.json values instead of a name. |
| context | integer | – | Tokens held in context: prompt plus conversation. |
| kv_cache | string | – | KV cache precision. fp16 is what most runtimes use; q8 halves the cache. |
| model | string | – | A model from list_models (id or name, e.g. "llama-3.3-70b" or "Llama 3.3 70B"), or any Hugging Face repo id (e.g. "Qwen/Qwen3-8B"), read live from its config.json. |
| quant | string | – | Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). Omit it for all 6. |
No output schema declared.
No examples provided.
list_gpus List GPUs ~64
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.
| Name | Type | Req | Description |
|---|---|---|---|
| search | string | – | Words to filter by, e.g. "qwen" or "24 GB". |
No output schema declared.
No examples provided.
list_models List models ~85
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.
| Name | Type | Req | Description |
|---|---|---|---|
| search | string | – | Words to filter by, e.g. "qwen" or "24 GB". |
No output schema declared.
No examples provided.
what_fits What fits my GPU? ~298
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 from list_gpus, or vram_gb for any other card.
| Name | Type | Req | Description |
|---|---|---|---|
| apple_silicon | boolean | – | vram_gb is Apple unified memory; the GPU can use about 75% of it by default. |
| bandwidth_gb_s | number | – | Memory bandwidth from the maker's spec, GB/s, for a speed ceiling. |
| context | integer | – | Tokens held in context: prompt plus conversation. |
| gpu | string | – | A GPU from list_gpus (id or name, e.g. "rtx-4090", "4090" or "M4 Max"). |
| quant | string | – | Weight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). q4 is the common default. |
| vram_gb | number | – | Memory of a card not in list_gpus, GB. For a Mac, its unified memory with apple_silicon: true. |
No output schema declared.
No examples provided.
How do I install the Nodegrove VRAM: can I run it? MCP server?
Nodegrove VRAM: can I run it? runs locally as an npm package, launched with npx -y @nodegrove/vram-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · @nodegrove/vram-mcp
claude mcp add io-nodegrove-vram-mcp -- npx -y @nodegrove/vram-mcp
What is the Nodegrove VRAM: can I run it? MCP server?
Nodegrove VRAM: can I run it? is an MCP server listed in the public MCP registry as io.nodegrove/vram-mcp. Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU. This page covers its npm package (@nodegrove/vram-mcp).
Is the Nodegrove VRAM: can I run it? MCP server safe to use?
Nodegrove VRAM: can I run it? scores 76 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 22 October 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Nodegrove VRAM: can I run it? MCP server expose?
Nodegrove VRAM: can I run it? exposes 6 tools: can_i_run, what_fits, estimate_vram, estimate_from_hf_repo, list_models, list_gpus. Their descriptions and schemas cost roughly 1,437 tokens of context every time the server is loaded.
Is the Nodegrove VRAM: can I run it? MCP server still maintained?
Nodegrove VRAM: can I run it? is still listed as active in the MCP registry. We last reached this channel on 22 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.