Documentation Index Fetch the complete documentation index at: https://docs.namastex.ai/llms.txt
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Overview Run powerful AI coding agents completely local - no API keys, no cloud services, no data leaving your machine. Perfect for privacy-sensitive work, learning, or unlimited free usage.
Privacy First : All code stays on your machine. No external API calls.
Supported Models OpenCode
OpenCode Open-source coding model
By : Open-source communitySize : 15B parametersContext : 16K tokensRequirements : 16GB+ RAM, GPU recommendedLicense : Apache 2.0 Best for: General coding, privacy-sensitive work Qwen Code
Qwen Code Alibaba’s open-source coder
By : Alibaba DAMO AcademySize : 7B, 14B, 32B parametersContext : 32K tokensRequirements : 8GB-64GB RAM depending on sizeLicense : Apache 2.0 Best for: Cost-conscious development, competitive with commercial Quick Start with Ollama Ollama makes running local models easy:
Install Ollama
# macOS brew install ollama # Linux curl https://ollama.ai/install.sh | sh # Windows # Download from https://ollama.ai
Pull Models
# OpenCode ollama pull opencode # Qwen Code (choose size) ollama pull qwen2.5-coder:7b # Small, fast ollama pull qwen2.5-coder:14b # Balanced ollama pull qwen2.5-coder:32b # Best quality
Configure Forge
Edit .forge/config.json: { "llms" : { "opencode" : { "type" : "ollama" , "model" : "opencode" , "endpoint" : "http://localhost:11434" }, "qwen" : { "type" : "ollama" , "model" : "qwen2.5-coder:32b" , "endpoint" : "http://localhost:11434" } } }
Test
forge task create \ --title "Test local model" \ --description "Print 'Hello from OpenCode!'" \ --llm opencode
Hardware Requirements Minimum Specs
Model RAM GPU Storage Speed Qwen 7B 8GB Optional 5GB Good Qwen 14B 16GB Recommended 10GB Better OpenCode 16GB Recommended 12GB Good Qwen 32B 32GB Required 25GB Best
Recommended Setup For best experience:
CPU: Modern multi-core (8+ cores) RAM: 32GB+ GPU: NVIDIA RTX 3060+ (12GB VRAM) or Apple Silicon M1/M2/M3 Storage: SSD with 50GB+ free space
Apple Silicon users : Metal acceleration makes M1/M2/M3 excellent for local models!
Configuration Basic Ollama Setup { "llms" : { "opencode" : { "type" : "ollama" , "model" : "opencode" , "endpoint" : "http://localhost:11434" } } }
Advanced Configuration { "llms" : { "qwen" : { "type" : "ollama" , "model" : "qwen2.5-coder:32b" , "endpoint" : "http://localhost:11434" , "options" : { "temperature" : 0.7 , "num_ctx" : 32768 , // Context window "num_predict" : 2048 , // Max output tokens "top_p" : 0.9 , "top_k" : 40 , "repeat_penalty" : 1.1 }, "timeout" : 120000 // 2 minutes } } }
GPU Acceleration Enable GPU support for faster inference:
{ "llms" : { "qwen" : { "type" : "ollama" , "model" : "qwen2.5-coder:32b" , "options" : { "num_gpu" : 1 , // Number of GPUs "main_gpu" : 0 , // Primary GPU index "low_vram" : false // Enable for under 8GB VRAM } } } }
Strengths of Local Models Complete Privacy
No Data Leakage
Code never leaves your machine
No API calls to external services
No telemetry or tracking
Perfect for sensitive codebases
Compliance-Ready
GDPR compliant by design
No third-party data sharing
Full audit trail
Meets enterprise security requirements
No Usage Limits # Unlimited usage! forge task create "Task 1" --llm qwen forge task create "Task 2" --llm qwen forge task create "Task 3" --llm qwen # ... as many as you want # No rate limits # No token costs # No monthly fees
No Internet Required Work anywhere:
✈️ On airplanes
🏔️ Remote locations
🔌 During outages
🔒 Air-gapped environments
Limitations Lower Quality Local models are less capable than cloud models:
Task Type Local Claude GPT-4 Simple fixes ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ Architecture ⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ Bug fixing ⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ Testing ⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐
Slower # Speed comparison: "Add authentication" Gemini Flash: 2m 15s ⚡ Claude Sonnet: 5m 22s GPT-4 Turbo: 6m 01s Qwen 32B: 12m 30s (local) OpenCode: 15m 45s (local)
Hardware Intensive
Requires powerful machine
GPU strongly recommended
High RAM usage
Slower on CPU-only
Best Use Cases Privacy-Sensitive Work # Medical records processing forge task create \ --title "Process patient data" \ --files "data/patients/*.csv" \ --llm qwen # Stays local!
Learning & Experimentation # Unlimited free usage for learning forge task create "Try approach 1" --llm opencode forge task create "Try approach 2" --llm opencode forge task create "Try approach 3" --llm opencode # ... no cost!
Air-Gapped Environments # Government, military, high-security environments # No internet connection required forge task create "Classified work" --llm qwen
Cost Reduction # After initial hardware investment # Zero ongoing costs forge task create "Feature 1" --llm qwen # $0 forge task create "Feature 2" --llm qwen # $0 forge task create "Feature 3" --llm qwen # $0
Model Comparison OpenCode vs Qwen
Feature OpenCode Qwen 7B Qwen 14B Qwen 32B Quality ⭐⭐⭐ ⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐ Speed ⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐ ⭐⭐ RAM 16GB 8GB 16GB 32GB Best for General Quick tasks Balanced Quality
Local vs Cloud
Feature Local (Qwen 32B) Claude Sonnet Gemini Pro Privacy ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐ Quality ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ Speed ⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ Cost $$$ hardware, $0 usage $0 hardware, $$$ usage $0 hardware, $$ usage
Use GPU # Check GPU usage nvidia-smi # Or on Mac system_profiler SPDisplaysDataType
Adjust Context Window { "llms" : { "qwen" : { "options" : { "num_ctx" : 16384 // Reduce for speed } } } }
Use Smaller Models for Simple Tasks # Simple task → Qwen 7B (fast) forge task create "Add comment" --llm qwen-7b # Complex task → Qwen 32B (quality) forge task create "Refactor architecture" --llm qwen-32b
Troubleshooting
Error : “Connection refused to localhost:11434”Solution :# Start Ollama ollama serve # Or as background service (Linux) systemctl start ollama # macOS (via brew services) brew services start ollama
Error : “Failed to allocate memory”Solutions :
Use smaller model (7B instead of 32B)
Close other applications
Enable low VRAM mode:
{ "llms" : { "qwen" : { "options" : { "low_vram" : true } } } }
Upgrade RAM
Issue : Model taking foreverSolutions :
Enable GPU acceleration
Use smaller model
Reduce context window
Close background apps
Check CPU usage (should be high)
Error : “Model ‘qwen2.5-coder:32b’ not found”Solution :# Pull the model ollama pull qwen2.5-coder:32b # List available models ollama list # Check model size before pulling ollama show qwen2.5-coder:32b
Cost Analysis Hardware Investment One-time costs: ├── Mid-range GPU (RTX 4060): $300 ├── Additional RAM (32GB): $100 └── SSD storage (1TB): $80 ──────────────────────────── Total: ~$480 Break-even vs Claude: $480 / $50/month = ~10 months of usage
Ongoing Costs Electricity: ├── GPU power: ~200W ├── Running 8hrs/day └── Cost: ~$5-10/month vs Cloud APIs: ├── Claude: $50-200/month typical usage ├── GPT-4: $80-300/month └── Gemini: $0-50/month (free tier)
Heavy users (>$100/month on APIs) break even quickly with local setup!
Best Practices
Use for Sensitive Work # Proprietary code forge task create \ --title "Refactor proprietary algorithm" \ --llm qwen # Stays local
Start Small Begin with Qwen 7B: ollama pull qwen2.5-coder:7b
Upgrade to 32B if needed
Monitor Resources # Check resource usage htop nvidia-smi # GPU # Adjust if needed
Combine with Cloud # Quick iteration: local forge task create "Try A" --llm qwen # Final polish: cloud forge task fork 1 --llm claude
Best of both worlds! Hybrid Strategy Combine local and cloud models:
Strategy 1: Privacy Tiers # Sensitive code → local forge task create \ --title "Process customer data" \ --llm qwen # Public code → cloud (faster, better) forge task create \ --title "Update README" \ --llm gemini
Strategy 2: Cost Optimization # Experimentation → local (free) forge task create "Try approach A" --llm qwen forge task create "Try approach B" --llm qwen forge task create "Try approach C" --llm qwen # Production → cloud (quality) forge task fork < winne r > --llm claude
Strategy 3: Network-Aware # Offline → local if ping -c 1 8.8.8.8 > /dev/null 2>&1 ; then LLM = gemini # Online, use fast cloud else LLM = qwen # Offline, use local fi forge task create "Task" --llm $LLM
Real-World Example Setup for Privacy-First Development # 1. Install Ollama brew install ollama # 2. Pull Qwen 32B (best quality) ollama pull qwen2.5-coder:32b # 3. Configure Forge cat > .forge/config.json << EOF { "llms": { "qwen": { "type": "ollama", "model": "qwen2.5-coder:32b", "endpoint": "http://localhost:11434", "options": { "num_gpu": 1, "num_ctx": 32768 } } } } EOF # 4. Start building (all local!) forge task create "Sensitive feature" --llm qwen
Next Steps
Install Ollama Get started with local models
Other Agents Compare with cloud agents
Privacy Workflows Privacy-first development patterns
Ollama Library Browse available models
