An intelligent decision engine that recommends optimal local LLM configurations and quantization levels based on hardware constraints and task requirements.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install local-model-quantization-router
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 local-model-quantization-router 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 Local Model Quantization Router?
The Local Model Quantization Router is a specialized tool designed to bridge the gap between hardware limitations and model performance requirements. It evaluates your available VRAM, system RAM, and specific task complexity to provide data-driven recommendations for running large language models locally. By integrating this into your Openclaw Skills library, you can ensure that your AI agent workloads are routed to the most efficient endpoint available, whether it be a local Ollama instance or a cloud-based fallback.
This skill serves as a critical pre-flight check for privacy-first or cost-sensitive deployments. It does not modify system configurations or download files itself; instead, it acts as a logic layer to help developers make informed decisions about model families and quantization levels like Q4_K_M or IQ4_XS based on the actual physical constraints of the host machine.
Local Model Quantization Router Use Cases
- Determining the highest possible quantization level for an LLM based on specific GPU VRAM.
- Implementing privacy-first workflows where sensitive data requires local-only model routing.
- Optimizing cost-performance by identifying tasks that can be handled by small local models versus high-end cloud providers.
- Predicting context window limits to prevent out-of-memory errors before starting long-running agentic tasks.
How Local Model Quantization Router Works
- The user provides hardware specifications such as VRAM, RAM, and the specific task complexity.
- The router processes these inputs against a set of logic rules tailored for Openclaw Skills deployments.
- The script analyzes privacy requirements to determine if cloud fallback is a permissible route.
- A recommendation is generated detailing the model family, suggested quantization, and the preferred local endpoint.
- The developer uses this output as evidence to configure the final routing strategy for their AI agent.
Local Model Quantization Router Setup
To use the router within your Openclaw Skills environment, ensure you have the python environment configured and run the script directly:
# Basic usage with CLI flags
python scripts/local_model_quantization_router.py --task "refactoring" --vram-gb 12 --complexity complex --privacy high
# Usage with a hardware profile JSON
python scripts/local_model_quantization_router.py --hardware config/my_rig.json --output recommendation.json
Local Model Quantization Router Data Schema & Taxonomy
The skill produces a structured recommendation based on the following taxonomy:
| Attribute | Description | Values |
|---|---|---|
| route | The primary deployment strategy | local-only, local-first, hybrid, cloud-required |
| model | The suggested model architecture | Qwen, Llama, Mistral |
| quantization | The recommended compression level | Q4_K_M, Q5_0, Q8_0, etc. |
| endpoint | The suggested local server interface | Ollama, vLLM, LM Studio |
| fallback | The recommended backup model | GPT-4o, Claude-3.5-Sonnet |
| reasons | Logical justification for the decision | Evidence-based strings |
Local Model Quantization Router Advanced Features
- Multi-parameter complexity assessment ranging from simple tasks to critical production workloads.
- Privacy-aware routing that strictly blocks cloud endpoints when regulated data handling is required.
- Support for custom hardware profiles via JSON to automate routing across different edge devices.
- Hybrid-mode logic that balances local processing with cloud-based quality fallbacks for context-heavy requests.
SKILL.md
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