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Install in OpenClaw
/install hardware-llm-optimizer
Description
Auto-detect PC hardware (CPU/GPU/RAM/VRAM) -> Determine max LLM parameters -> Recommend models (3B/7B/8B/13B/34B/70B) + quantization + deployment tools + bot...
README (SKILL.md)
Hardware LLM Optimizer
Detects PC hardware configuration and recommends which large language models can run.
Features
- Auto-detect: CPU, RAM, GPU (NVIDIA/AMD), VRAM
- Calculate: Maximum runnable model size
- Quantization: FP16 / 8bit / 4bit / 2bit recommendation
- Model Suggestion: Llama 2/3, Qwen, Mistral, Phi, Gemma, Yi, etc.
- Bottleneck Analysis: System constraint diagnosis
- Deployment Tools: Ollama, Llama.cpp, vLLM, Chatbox
- Optimization Tips: Low VRAM solutions
- Minimum Config Table: 3B/7B/13B/34B/70B requirements
Usage
When user asks about running LLMs on their computer:
检测电脑配置
大模型推荐
能跑什么模型
硬件检测
LLM优化
Quick Run
python3 skills/hardware-llm-optimizer/detect.py
Requirements
- Python 3.8+
- psutil:
pip install psutil - nvidia-smi (optional, for NVIDIA GPU detection)
Minimum Config Reference
| Model | Min VRAM | Rec VRAM | Quantization |
|---|---|---|---|
| 3B | 2GB | 4GB | Q4 |
| 7B | 6GB | 8GB | Q4/Q8 |
| 13B | 10GB | 16GB | Q4/Q8 |
| 34B | 20GB | 32GB | Q4 |
| 70B | 40GB | 80GB | Q4 |
Chinese Interface
This skill outputs in Chinese for user convenience.
Usage Guidance
This skill appears to do what it claims: run locally, detect hardware, and print recommendations. Before installing/running: (1) review the detect.py yourself (it's included) and confirm you're comfortable executing it, (2) be aware it will reveal local hardware details (CPU/GPU/RAM) to whatever component runs the skill — if your agent forwards outputs to external services, those details could be transmitted, and (3) install psutil (pip install psutil) and ensure nvidia-smi is available if you want GPU detection. If you want extra caution, run the script in a local sandbox or VM first.
Capability Analysis
Type: OpenClaw Skill
Name: hardware-llm-optimizer
Version: 1.1.0
The hardware-llm-optimizer skill is a legitimate tool designed to detect system hardware (CPU, RAM, GPU) and recommend suitable Large Language Models. The core logic in detect.py uses standard libraries like psutil and subprocess (to call nvidia-smi) to gather hardware specifications, which is consistent with its stated purpose in SKILL.md. No evidence of data exfiltration, unauthorized network access, or malicious prompt injection was found.
Capability Assessment
Purpose & Capability
Name/description (detect hardware, recommend models/quantization/deployment) align with included files: SKILL.md instructs running detect.py and detect.py inspects CPU, RAM, GPU and gives model/quantization recommendations. No unrelated credentials, binaries, or services are requested.
Instruction Scope
SKILL.md directs the agent to run the bundled detect.py. The script only reads local system info (psutil), checks /proc/version for WSL, and calls nvidia-smi for GPU details — all within the stated purpose. It does not access external endpoints, env vars, or other user files beyond /proc/version.
Install Mechanism
No install spec; this is instruction-only with a bundled Python script. The only required third-party package is psutil (documented in SKILL.md). The script does not download or execute code from remote URLs.
Credentials
No environment variables, credentials, or config paths are requested. The script uses local APIs and optional nvidia-smi; this is proportionate to hardware-detection functionality.
Persistence & Privilege
Skill is not always-enabled, does not attempt to modify other skills or agent-wide settings, and does not persist credentials. It merely prints local detection results.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install hardware-llm-optimizer - After installation, invoke the skill by name or use
/hardware-llm-optimizer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
hardware-llm-optimizer 1.1.0
- Expanded model and quantization recommendation table to include 3B models and minimum config details.
- Added bottleneck analysis and more detailed optimization tips.
- Updated feature list: deployment tools section (Ollama, Llama.cpp, vLLM, Chatbox) and system constraint diagnosis.
- Improved documentation and usage instructions.
- Maintains Chinese output interface.
v1.0.0
Initial release of hardware-llm-optimizer:
- Automatically detects PC hardware: CPU, RAM, GPU (NVIDIA/AMD), and VRAM.
- Analyzes hardware to determine the maximum size of LLM models supported.
- Recommends suitable LLM models (7B/8B/13B/34B/70B), quantization options, and deployment tools.
- Provides additional features: Ollama and llama.cpp compatibility checks, local deployment tips, and low VRAM optimizations.
- Designed with a Chinese interface for user interaction.
Metadata
Frequently Asked Questions
What is Hardware Llm Optimizer?
Auto-detect PC hardware (CPU/GPU/RAM/VRAM) -> Determine max LLM parameters -> Recommend models (3B/7B/8B/13B/34B/70B) + quantization + deployment tools + bot... It is an AI Agent Skill for Claude Code / OpenClaw, with 91 downloads so far.
How do I install Hardware Llm Optimizer?
Run "/install hardware-llm-optimizer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Hardware Llm Optimizer free?
Yes, Hardware Llm Optimizer is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Hardware Llm Optimizer support?
Hardware Llm Optimizer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Hardware Llm Optimizer?
It is built and maintained by SMS (@smseow001); the current version is v1.1.0.
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