# genpark-quantized-model-vram-tensor-parallel-estimator-skill

> genpark-quantized-model-vram-tensor-parallel-estimator-skill — alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill. Use this tool when you need to estimate the VRAM footprint of quantized models and optimize tensor parallelism, solving problems related to model deployment and scalability. It takes model specifications as input and outputs estimated VRAM usage and parallelization recommendations. Ideal for use cases involving large-scale model training and deployment on limited GPU resources.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill

## Description

Quantized model VRAM footprint & tensor parallelism estimator (llama.cpp)

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-quantized-model-vram-tensor-parallel-estimator-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-quantized-model-vram-tensor-parallel-estimator-skill/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt