An intelligent hardware-to-LLM matching tool that optimizes local AI model selection and quantization based on your specific system specs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hardware-llm-optimizer-v2
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hardware-llm-optimizer-v2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Hardware LLM Optimizer?
Hardware LLM Optimizer is a powerful utility designed to bridge the gap between local hardware capabilities and Large Language Model requirements. Built on the llmfit core, this skill automatically analyzes your CPU, GPU, RAM, and VRAM to provide data-driven recommendations for the most suitable models and quantization schemes. It is a vital part of the Openclaw Skills ecosystem for developers running local AI.
By utilizing a library of over 100 models, it removes the guesswork from setting up local environments. Whether you are using NVIDIA GPUs or standard CPUs, this tool provides precise estimates for speed (Tokens Per Second) and memory requirements, ensuring you get the best possible performance out of your machine using Openclaw Skills.
Hardware LLM Optimizer Use Cases
- Identifying which local LLMs can run on your current hardware configuration.
- Finding the optimal quantization level (e.g., Q4_K_M vs Q8_0) for a specific GPU.
- Simulating hardware upgrades to see how a 16GB or 24GB VRAM card would impact model performance.
- Filtering model recommendations based on specific tasks like coding, chatting, or embedding.
- Benchmarking potential TPS for different model architectures before downloading large files.
How Hardware LLM Optimizer Works
- The skill executes a hardware scan to detect available system resources including VRAM and system RAM.
- It cross-references detected hardware against a comprehensive database of 100+ LLMs supported by Openclaw Skills.
- The tool calculates the fit level (Perfect, Good, or Marginal) for various models based on memory requirements.
- It generates a ranked list of recommendations including the best quantization format and estimated speed.
- Users can output results to a TUI for interactive exploration or JSON for automated pipeline integration.
Hardware LLM Optimizer Setup
The Hardware LLM Optimizer is primarily powered by the llmfit engine. To get started with this part of Openclaw Skills, run the following:
# Install llmfit via the official installation script
curl -fsSL https://llmfit.axjns.dev/install.sh | sh
# Run the recommendation engine
llmfit recommend
The binary is typically located at /usr/local/bin/llmfit.
Hardware LLM Optimizer Data Schema & Taxonomy
The skill provides structured data for each recommended model to help you manage your Openclaw Skills workflow:
| Field | Description |
|---|---|
| name | The specific model identifier |
| parameter_count | Total model parameters (e.g., 8B, 70B) |
| best_quant | Recommended quantization level (FP16, Q8_0, Q4_K_M, etc.) |
| score | A comprehensive ranking score for the match |
| estimated_tps | Predicted tokens per second performance |
| memory_required_gb | Minimum VRAM/RAM needed for the recommended setup |
| fit_level | Qualitative match score (Perfect, Good, or Marginal) |
Hardware LLM Optimizer Advanced Features
- Hardware Simulation: Use flags like
--memory 16Gor--ram 64Gto predict performance on hardware configurations you do not currently own. - JSON Output: Support for
--jsonallows other AI agents or automated scripts to programmatically consume recommendation data within the Openclaw Skills environment. - Use-Case Filtering: Tailor recommendations specifically for specialized tasks by using the
--use-caseflag for coding, embedding, or general chat. - TUI Mode: Launch an interactive terminal user interface for a visual exploration of hardware-to-model compatibility.
SKILL.md
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