# llmfit

> llmfit — alexsjones-llmfit. Use this tool when you need to optimize local deployment of Large Language Models (LLMs) by detecting available hardware resources and recommending best-fit models with optimal quantization, solving issues of compatibility and performance. It takes into account inputs such as RAM, CPU, and GPU/VRAM capacities, and outputs tailored model recommendations. This tool is ideal for use cases where efficient local LLM deployment is crucial, such as in edge AI applications or resource-constrained environments.

Canonical page: https://skillsregistry.net/skills/alexsjones-llmfit  
JSON: https://api.skillsregistry.net/v1/skills/alexsjones-llmfit

## Description

Detect local hardware (RAM, CPU, GPU/VRAM) and recommend the best-fit local LLM models with optimal quantization.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** iot-hardware
- **Updated:** 2026-09-28

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/alexsjones-llmfit)

## 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": "alexsjones-llmfit"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alexsjones-llmfit` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alexsjones-llmfit/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