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:

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 \x3Cname> — 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

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 }
}