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llmfit-advisor

Analyze local hardware to recommend LLMs with quantization and speed estimates.

Updated Feb 26, 2026

Search Tags:#llm #performance tuning #quantization #ollama #local models #hardware advisor #lm studio

One-click install

npx skills add https://github.com/CxxxxDxxxF/project-blackout --skill llmfit-advisor

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: llmfit-advisor
Source: https://github.com/CxxxxDxxxF/project-blackout/tree/main/tools/llmfit/skills/llmfit-advisor
Command: npx skills add https://github.com/CxxxxDxxxF/project-blackout --skill llmfit-advisor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users identify which local Large Language Models (LLMs) are compatible with their specific hardware, recommending optimal configurations for performance and fit.

Core Features & Use Cases

Hardware Detection: Automatically detects CPU, RAM, and GPU/VRAM specifications.
Model Recommendation: Suggests LLMs based on hardware capabilities, including quantization and fit levels.
Use Case: A user wants to run LLMs locally but is unsure which models their machine can handle. This Skill analyzes their system and provides a ranked list of suitable models, along with recommended quantization settings and estimated performance.

Quick Start

Run llmfit to recommend the best local models for your hardware.

Frequently Asked Questions about llmfit-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I know which local LLMs will run on my hardware?▼

To find local LLMs that fit your hardware, analyze your CPU, RAM, and GPU/VRAM specifications to receive a ranked list of suitable models with recommended quantization settings and estimated performance.

What is the best way to choose a quantization level for local models?▼

Choosing optimal quantization for local models requires matching the configuration to your specific hardware capabilities, ensuring the model fits within your system's memory limits while maximizing inference speed.

Can I use this hardware analysis with Ollama and LM Studio?▼

Yes, you can use this hardware analysis with local inference providers like Ollama and LM Studio by utilizing the detailed system and model output provided in JSON format for seamless integration.

How do I filter local model recommendations by specific use cases?▼

You can filter local model recommendations by specific use cases and fit levels, allowing you to select models that not only match your hardware but also align with your intended application.

Why does my computer struggle to run large language models locally?▼

Your computer struggles to run large language models locally likely due to insufficient CPU, RAM, or GPU/VRAM. Analyzing your system specifications helps identify compatible models and optimal quantization settings to prevent performance issues.

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