# Nodegrove VRAM: can I run it? (npm · @nodegrove/vram-mcp)

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

- Trust score: 76/100 (medium)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-22

## Components

- remote · `mcp.nodegrove.io`: 74/100, [markdown](https://verifymcp.io/servers/io-nodegrove-vram-mcp/mcp.md), [page](https://verifymcp.io/servers/io-nodegrove-vram-mcp/mcp)
- npm · `@nodegrove/vram-mcp`: 76/100 (this document), [markdown](https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp.md), [page](https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp)

## Channel facts

- Registry: `npm`
- Package: `@nodegrove/vram-mcp`
- Version: `1.1.4`
- Transport: `stdio`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-22.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - No install/post-install scripts declared.
  - 0 of 3 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 35/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - License check failed: the license ((MIT AND CC-BY-4.0)) isn't a recognized OSI-approved license.
  - Actively maintained (last published 2 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 66/100
  - AI-judged instruction clarity (excellent).
  - 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.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 63/100
  - Stability observed for 19 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 6 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### 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.

### Claude

```bash
claude mcp add io-nodegrove-vram-mcp -- npx -y @nodegrove/vram-mcp
```

### Cursor

```json
{
  "mcpServers": {
    "io-nodegrove-vram-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@nodegrove/vram-mcp"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "io-nodegrove-vram-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@nodegrove/vram-mcp"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add io-nodegrove-vram-mcp -- npx -y @nodegrove/vram-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "io-nodegrove-vram-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "@nodegrove/vram-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add io-nodegrove-vram-mcp --command npx --arg -y --arg @nodegrove/vram-mcp
```

### Hermes

```yaml
mcp_servers:
  io-nodegrove-vram-mcp:
    command: "npx"
    args: ["-y", "@nodegrove/vram-mcp"]
```

### Netclaw

```json
{
  "McpServers": {
    "io-nodegrove-vram-mcp": {
      "Transport": "stdio",
      "Command": "npx",
      "Arguments": [
        "-y",
        "@nodegrove/vram-mcp"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add io-nodegrove-vram-mcp -t stdio -c npx -a -y @nodegrove/vram-mcp
```

### Other

```json
{
  "mcpServers": {
    "io-nodegrove-vram-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@nodegrove/vram-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-22 (score 76, +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.

### 2026-10-20 (score 75, +16)

- [security improvement] Malware scan: unverified → pass

### 2026-10-19 (score 59, −15)

- [security regression] Malware scan: pass → unverified
- [functional] Package version: 1.1.3 → 1.1.4

### 2026-10-18 (score 74, +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.

### 2026-10-17 (score 73, +15)

- [security improvement] Malware scan: unverified → pass

### 2026-10-16 (score 58, −14)

- [security regression] Malware scan: pass → unverified
- [functional] Package version: 1.1.2 → 1.1.3

### 2026-10-13 (score 72, +35)

- [security improvement] Injection markers: unverified → pass
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Stability: unverified → 0.33

### 2026-10-11 (score 37, −30)

- [security regression] 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.
- [functional regression] Capabilities: pass → unverified
- [functional regression] Tool coverage: 100 → unverified
- [functional] First check of Schema quality: unverified
- [functional] Package version: 1.0.0 → 1.1.2

## MCP tools (6)

### `can_i_run` (~480 tokens)

Can I run it?

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.

Input parameters:

- `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.

### `what_fits` (~298 tokens)

What fits my GPU?

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.

Input parameters:

- `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.

### `estimate_vram` (~312 tokens)

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).

Input parameters:

- `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.

### `estimate_from_hf_repo` (~198 tokens)

Estimate VRAM from a Hugging Face 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 reviewed; anything the reader cannot model is listed in warnings.

Input parameters:

- `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, required): Hugging Face repo id, e.g. "Qwen/Qwen3-8B", or its huggingface.co URL.

### `list_models` (~85 tokens)

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.

Input parameters:

- `search` (string): Words to filter by, e.g. "qwen" or "24 GB".

### `list_gpus` (~64 tokens)

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 parameters:

- `search` (string): Words to filter by, e.g. "qwen" or "24 GB".

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp#diagnostics

## Score history

- 2026-10-22: 76
- 2026-10-21: 75
- 2026-10-20: 75
- 2026-10-19: 59
- 2026-10-18: 74
- 2026-10-17: 73
- 2026-10-16: 58
- 2026-10-15: 72
- 2026-10-14: 72
- 2026-10-13: 72
- 2026-10-12: 37
- 2026-10-11: 37
- 2026-10-05: 67
- 2026-10-04: 67
- 2026-10-03: 52

## Common questions

### 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.

## Links

- npm package: https://www.npmjs.com/package/@nodegrove/vram-mcp
- Socket report: https://socket.dev/npm/package/@nodegrove/vram-mcp
- Repository: https://github.com/nodegrove/vram-mcp
- Website: https://nodegrove.io/mcp
- Changelog RSS feed: https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp.json
- HTML version of this page: https://verifymcp.io/servers/io-nodegrove-vram-mcp/nodegrove-vram-mcp