Local Model Optimizer

Auto-detect hardware → recommend models → configure Ollama → set up hybrid cloud/local routing.

Quick Start

# Full auto-setup: detect hardware, install Ollama, recommend + pull model, configure routing
python3 scripts/local-model-optimizer.py auto

# Hardware detection only
python3 scripts/local-model-optimizer.py detect

# Recommend models for your hardware (no install)
python3 scripts/local-model-optimizer.py recommend

# Set up hybrid routing (cloud for complex tasks, local for simple ones)
python3 scripts/local-model-optimizer.py routing

# Cost comparison: local vs cloud
python3 scripts/local-model-optimizer.py cost

Commands

`auto` — Full Automated Setup

1. Detects GPU (NVIDIA/AMD/Apple Silicon), VRAM, RAM, CPU cores

2. Queries Ollama model registry for compatible models

3. Recommends top 3 models ranked by benchmark/size ratio

4. Installs Ollama if not present

5. Pulls recommended model

6. Configures OpenClaw provider entry

7. Sets up hybrid routing rules

8. Runs verification test

`detect` — Hardware Detection

Reports:

• GPU model, VRAM, driver version (NVIDIA/AMD/Apple)
• System RAM (total/available)
• CPU model, core count, architecture
• Estimated model size capacity
• Compatibility tier: Tiny (≤4GB) / Small (4-8GB) / Medium (8-16GB) / Large (16-32GB) / XL (32GB+)

`recommend` — Model Recommendations

Based on hardware tier, recommends from:

| Tier | VRAM | Models |

|------|------|--------|

| Tiny | ≤4GB | Gemma 4 E2B, Phi-3.5 Mini, Qwen2.5-3B |

| Small | 4-8GB | Gemma 4 E4B, Llama 3.1 8B, Mistral 7B |

| Medium | 8-16GB | Gemma 4 12B, Llama 3.1 8B Q8, CodeGemma |

| Large | 16-32GB | Gemma 4 27B, Llama 3.1 70B Q4, Mixtral 8x7B |

| XL | 32GB+ | Gemma 4 27B Q8, Llama 3.1 70B Q8, DeepSeek V2 |

See references/model-matrix.md for full benchmark comparisons.

`routing` — Hybrid Cloud/Local Routing

Configures OpenClaw to route requests intelligently:

• Local: Simple Q&A, summarization, code completion, memory operations
• Cloud: Complex reasoning, multi-step planning, code generation, creative writing

Options:

• --strategy cost — minimize API spend (prefer local)
• --strategy quality — maximize output quality (prefer cloud)
• --strategy balanced — default, smart routing based on task complexity
• --cloud-provider — which cloud provider for fallback (default: anthropic)

`cost` — Cost Analysis

Calculates monthly savings based on:

• Current API usage pattern (reads from OpenClaw logs if available)
• Estimated electricity cost for local inference
• Token throughput comparison
• Break-even analysis for hardware investment

Important Notes

Ollama Auto-Install

The auto command will install Ollama if not already present by running curl -fsSL https://ollama.com/install.sh | sh. This downloads and executes a shell script from ollama.com. Review the script at https://ollama.com/install.sh before running if you prefer to audit first, or install Ollama manually.

File Access Scope

This skill reads and writes ~/.openclaw/openclaw.json to configure hybrid routing provider entries. It also writes its own config to ~/.openclaw/local-model-config.json. Both files are modified only during auto and routing commands.

Configuration

The optimizer writes to ~/.openclaw/local-model-config.json:

{
  "hardware": { "gpu": "...", "vram_gb": 16, "ram_gb": 32, "tier": "Large" },
  "model": { "name": "gemma4:27b", "quantization": "Q4_K_M", "size_gb": 15.2 },
  "routing": { "strategy": "balanced", "local_tasks": [...], "cloud_tasks": [...] },
  "performance": { "tokens_per_sec": 42, "first_token_ms": 180 }
}