Local Model Quantization Router

Recommend local LLM model routes and quantization levels using hardware, privacy, task complexity, context size, and budget constraints. Use for Qwen/Ollama/...

Use this skill to choose between local quantized models and cloud fallback before running OpenClaw workloads.

Workflow

1. Describe available hardware and task requirements.
2. Run scripts/local_model_quantization_router.py with CLI flags or JSON input.
3. Review the recommended model family, quantization level, endpoint, fallback, and risk notes.
4. Use the output as routing evidence for local-first or privacy-first deployments.

Parameters

• --task TEXT: Task summary.
• --complexity {simple,standard,complex,critical}.
• --privacy {low,normal,high,regulated}.
• --vram-gb FLOAT: GPU memory available.
• --ram-gb FLOAT: System memory available.
• --context-tokens INT: Required context window.
• --hardware PATH: Optional JSON with vram_gb, ram_gb, cpu_only.
• --output PATH: Optional JSON output path.

Outputs

• route: local-only, local-first, hybrid, or cloud-required.
• model: Recommended model family.
• quantization: Suggested quant level.
• endpoint: Suggested local endpoint type.
• fallback: Safer fallback when quality or context is insufficient.
• reasons: Evidence for the decision.

No model is downloaded and no config file is changed.