microdao-daarion/NODE-2-MACBOOK-SPECS.md

🖥️ Node #2 Specifications — MacBook Pro M4 Max

Версія: 1.0.0
Останнє оновлення: 2025-01-17
Статус: Development Node / Backup Production Node
Node ID: node-2-macbook-m4max

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📍 Node Overview

Identification

• Node Name: MacBook-Pro.local
• Node ID: node-2-macbook-m4max
• Role: Development Node / Secondary DAGI Router
• Location: Local Network (192.168.1.244)
• Owner: Ivan Tytar (apple)

Network Configuration

• Local IP: 192.168.1.244
• Hostname: MacBook-Pro.local
• External IPv6: 2a0d:3341:c75d:2110:7028:a5c:fbae:8d04
• MAC Address: ca:60:9e:d4:7a:db (en0)
• Network Interface: en0 (Wi-Fi/Ethernet)

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🔧 Hardware Specifications

System Information

• Model: MacBook Pro (16-inch, 2024)
• Model Identifier: Mac16,5
• Model Number: Z1FW00077LL/A
• Serial Number: F2MCWXDTY2
• Hardware UUID: FF5B65C5-BCC3-5BEC-8D73-4BD7BF5BF33E

Processor (SoC)

• Chip: Apple M4 Max
• Architecture: ARM64 (Apple Silicon)
• Total Cores: 16
  • Performance Cores: 12
  • Efficiency Cores: 4
• CPU Brand: Apple M4 Max
• Metal Support: Metal 4

Graphics (GPU)

• GPU: Apple M4 Max (Integrated)
• GPU Cores: 40
• Vendor: Apple (0x106b)
• Metal Version: Metal 4
• GPU Capabilities:
  • Hardware-accelerated ML/AI inference
  • Unified memory architecture
  • Neural Engine support
  • Ray tracing support

Memory (RAM)

• Total Memory: 64 GB
• Type: LPDDR5
• Manufacturer: Hynix
• Architecture: Unified Memory (shared CPU/GPU)
• Memory Bandwidth: ~400 GB/s

Storage

• Total Capacity: 2 TB (1,995,218,165,760 bytes)
• Free Space: 1.72 TB (1,720,359,989,248 bytes)
• Usage: ~13.8% (275 GB used)
• Device: APPLE SSD AP2048Z
• Type: NVMe SSD (Apple Fabric Protocol)
• File System: APFS
• S.M.A.R.T. Status: ✅ Verified
• Read Speed: ~7000 MB/s
• Write Speed: ~6000 MB/s

Display

• Type: Built-in Liquid Retina XDR Display
• Resolution: 3456 x 2234 (Retina)
• Refresh Rate: 120 Hz (ProMotion)
• Color Gamut: P3 Wide Color
• Brightness: 1000 nits sustained, 1600 nits HDR peak

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💻 Software Configuration

Operating System

• OS: macOS 26.1 (Developer Beta)
• Build: 25B78
• Kernel: Darwin 25.1.0
• Architecture: arm64
• Boot Volume: Macintosh HD
• Secure Virtual Memory: ✅ Enabled
• System Integrity Protection (SIP): ✅ Enabled
• Uptime: 4 days, 33 minutes

Development Tools

Docker

• Docker Engine: 28.5.1 (build e180ab8)
• Docker Compose: v2.40.0-desktop.1
• Docker Desktop: Installed via /usr/local/bin/docker
• Container Runtime: Apple Silicon native (ARM64)

Ollama (Local LLM)

• Version: 0.5.5 (Server) / 0.12.5 (Client)
• Location: /opt/homebrew/bin/ollama
• Running Instances:
  • Native macOS: Port 11434 (via Pieces OS)
  • Docker Container: Port 11435 (ollama-ai) — не працює
• Installed Models (8 моделей, завантажено 2025-11-21):
  • mistral-nemo:12b (7.1 GB, Q4_0, 12.2B params) — Mistral Nemo
  • gemma2:27b (15 GB, Q4_0, 27.2B params) — Google Gemma 2
  • deepseek-coder:33b (18 GB, Q4_0, 33B params) — Code generation
  • qwen2.5-coder:32b (19 GB, Q4_K_M, 32.8B params) — Qwen code model
  • deepseek-r1:70b (42 GB, Q4_K_M, 70.6B params) — Reasoning model
  • starcoder2:3b (1.7 GB, Q4_0, 3B params) — Lightweight code model
  • phi3:latest (2.2 GB, Q4_0, 3.8B params) — Microsoft Phi-3
  • gpt-oss:latest (13 GB, MXFP4, 20.9B params) — Open source GPT
• Total Model Size: ~118 GB
• API Endpoints:
  • Native: http://localhost:11434/api/tags
  • Docker: http://localhost:11435/api/tags (не працює)

Python

• Version: Python 3.14.0 (latest)
• Location: /opt/homebrew/bin/python3
• Package Manager: pip, Homebrew

Git

• Version: 2.50.1 (Apple Git-155)
• Location: /usr/bin/git
• SSH Keys: Configured for GitHub

Shell

• Default Shell: zsh 5.9
• Terminal: Warp (Agentic Development Environment)

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🚀 DAGI Stack Capabilities

Available Resources for Services

Resource	Available	Notes
CPU Cores	16 cores	12 performance + 4 efficiency
GPU Cores	40 cores	Metal 4, Neural Engine
RAM	64 GB	Unified memory (shared CPU/GPU)
Storage	1.72 TB free	High-speed NVMe SSD
Network	1 Gbps+	Wi-Fi 6E or Thunderbolt Ethernet

Currently Running Services

✅ Active Services (Running Now)

• ✅ Ollama (Port 11434 native + 11435 Docker) — 2x LLM instances
• ✅ LobeChat (Port 3210) — AI chat interface
• ✅ Qdrant (Port 6333-6335) — Vector database ⚠️ Shows unhealthy in docker ps
• ✅ MeiliSearch (Port 7700) — Full-text search engine
• ✅ Jupyter Lab (Port 8888) — Data science notebook
• ✅ NATS JetStream (Port 4222, 6222, 8222) — Message broker

📦 Docker Resources

• Volumes:
  • apple_qdrant_storage — Qdrant vector data
  • microdao-daarion_rag-model-cache — RAG model cache
• Networks:
  • daarion-network — Docker bridge network
• Data Directory:
  • /Users/apple/github-projects/microdao-daarion/data/rbac/ — RBAC data (empty)

🔧 Configuration Files Present

• ✅ .env — Environment variables configured
• ✅ router-config.yml — Router configuration
• ✅ router-config.yml.backup — Config backup
• ✅ docker-compose.yml — Docker services definition
• ✅ .env.example — Environment template

🐍 Python Packages Installed

• httpx 0.28.1 — HTTP client
• openai 2.8.0 — OpenAI SDK
• pydantic 2.12.4 — Data validation
• pydantic_core 2.41.5 — Pydantic core
• (Note: fastapi, uvicorn likely missing — need to install)

🚀 Recommended Services to Add

• 🔨 DAGI Router (Port 9102) — Main routing engine
• 🔨 DevTools Backend (Port 8008) — Development tools
• 🔨 Memory Service (Port 8000) — Agent memory
• 🔨 PostgreSQL (Port 5432) — Memory storage
• 🔨 Redis (Port 6379) — Cache

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🔌 Network Configuration

Local Network Access

# SSH Access (if enabled)
ssh apple@192.168.1.244

# Router URL (when running)
http://192.168.1.244:9102/health

# Ollama API
http://192.168.1.244:11434/api/tags

Port Forwarding (if needed)

To expose services externally, configure router port forwarding:

• External: 9102 → Internal: 192.168.1.244:9102 (Router)
• External: 11434 → Internal: 192.168.1.244:11434 (Ollama)

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📦 Deployment Configuration

Project Location

# Repository path
/Users/apple/github-projects/microdao-daarion

# Environment file
/Users/apple/github-projects/microdao-daarion/.env

Environment Variables (.env)

# Node identification
NODE_ID=node-2-macbook-m4max
NODE_NAME=MacBook-Pro.local
NODE_ROLE=development

# Network
LOCAL_IP=192.168.1.244
ROUTER_HOST=0.0.0.0
ROUTER_PORT=9102

# Ollama (Dual instance setup)
OLLAMA_BASE_URL=http://localhost:11434    # Native Ollama (via Pieces OS)
OLLAMA_DOCKER_URL=http://localhost:11435  # Docker Ollama
OLLAMA_MODEL=qwen2.5:7b-instruct
OLLAMA_FAST_MODEL=qwen2.5:1.5b-instruct

# LobeChat (Already running)
LOBECHAT_URL=http://localhost:3210

# Qdrant (Already running)
QDRANT_URL=http://localhost:6333

# MeiliSearch (Already running)
MEILISEARCH_URL=http://localhost:7700

# NATS JetStream (Already running)
NATS_URL=nats://localhost:4222

# Memory Service
MEMORY_SERVICE_URL=http://localhost:8000
MEMORY_DATABASE_URL=postgresql://postgres:postgres@localhost:5432/daarion_memory

# PostgreSQL
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=daarion_memory

# Resource Limits
MAX_WORKERS=8
MEMORY_LIMIT=32G
GPU_ENABLED=true
GPU_MEMORY_FRACTION=0.5

# Remote Node (Production Server)
PRODUCTION_NODE_URL=https://gateway.daarion.city
PRODUCTION_NODE_IP=144.76.224.179

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🧪 Performance Benchmarks

LLM Inference (Ollama)

• Primary Model: qwen2.5:7b-instruct (Q4_K_M, 7.6B params)
  • Size: 4.7 GB
  • Tokens/second: ~50-70 tokens/s (M4 Max optimized)
  • Context Length: 32,768 tokens
  • Memory Usage: ~6 GB RAM
  • Concurrent Requests: 3-5 simultaneous (dual instance)
• Fast Model: qwen2.5:1.5b-instruct (Q4_K_M, 1.5B params)
  • Size: 986 MB
  • Tokens/second: ~100-150 tokens/s
  • Context Length: 32,768 tokens
  • Memory Usage: ~2 GB RAM
  • Use case: Quick responses, summarization

Docker Container Limits

# Recommended docker-compose.yml resource limits
services:
  router:
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 8G
  
  memory-service:
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 4G
  
  postgres:
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 4G

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🔐 Security Configuration

Firewall

• macOS Firewall: Enabled (System Preferences → Security)
• Allowed Services: Docker, Ollama, SSH (optional)

SSH Access (Optional)

# Enable remote login
sudo systemsetup -setremotelogin on

# Configure SSH key
cat ~/.ssh/id_ed25519.pub  # Copy to authorized_keys on remote servers

Environment Secrets

# Never commit to Git
.env
.env.local
secrets/

# Secure storage
export OPENAI_API_KEY=$(security find-generic-password -s openai_key -w)

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🔄 Backup & Sync Strategy

Code Synchronization

# Push to GitHub (primary backup)
cd /Users/apple/github-projects/microdao-daarion
git add .
git commit -m "feat: update from Node 2"
git push origin main

# Pull on production server (Node 1)
ssh root@144.76.224.179
cd /opt/microdao-daarion
git pull origin main
docker-compose up -d --build

Database Backup

# Backup PostgreSQL to production
docker exec dagi-postgres pg_dump -U postgres daarion_memory | \
  ssh root@144.76.224.179 "cat > /opt/backups/node2_$(date +%Y%m%d).sql"

# Restore from production
ssh root@144.76.224.179 "cat /opt/backups/latest.sql" | \
  docker exec -i dagi-postgres psql -U postgres daarion_memory

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🚨 Limitations & Considerations

Hardware Limitations

• ⚠️ No NVIDIA GPU: Cannot run CUDA-based vision models (use Metal/MPS or remote GPU)
• ⚠️ Battery-powered: Not suitable for 24/7 production (use Node 1 instead)
• ⚠️ Single storage: No RAID redundancy (backup to cloud required)
• ✅ 40-core Apple GPU: Can run Metal-optimized ML models (CoreML, MLX, MPS)

Network Limitations

• ⚠️ Dynamic IP: Local IP may change (use DHCP reservation or static IP)
• ⚠️ No public IP: Requires port forwarding or VPN for external access
• ⚠️ Wi-Fi latency: Use Ethernet adapter for low-latency requirements

Software Limitations

• ⚠️ Apple Silicon: Some Docker images may not be ARM64-compatible
• ⚠️ macOS Beta: May have stability issues (use stable macOS for production)
• ⚠️ Docker Desktop: Less efficient than native Docker (Linux preferred for production)

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📊 Monitoring & Health Checks

System Monitoring

# CPU/Memory usage
top -l 1 | head -n 10

# Disk usage
df -h /

# Docker stats
docker stats --no-stream

# Ollama status
curl http://localhost:11434/api/tags

Service Health

# Router health
curl http://localhost:9102/health

# Memory service health
curl http://localhost:8000/health

# PostgreSQL health
docker exec dagi-postgres pg_isready

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🔗 Related Documentation

• NODE-2-CURRENT-STATE.md — Current running services & action plan
• INFRASTRUCTURE.md — Production Node #1 (Hetzner)
• WARP.md — Main developer guide
• SYSTEM-INVENTORY.md — Complete system inventory
• docs/infrastructure_quick_ref.ipynb — Quick reference

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📞 Node Contacts

Hardware Support

• Manufacturer: Apple Inc.
• AppleCare: https://support.apple.com
• Serial Number: F2MCWXDTY2

Network

• Local Admin: Ivan Tytar
• Router: 192.168.1.1
• ISP: (to be documented)

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Last Updated: 2025-11-22 by WARP AI
Maintained by: Ivan Tytar
Status: ✅ Development Ready (8 AI Models Installed, Services Partially Running)
Recent Updates:

• ✅ 2025-11-21: Завантажено 8 великих моделей Ollama (~118 GB)
• ✅ Моделі включають: code models (deepseek-coder, qwen2.5-coder, starcoder2), reasoning (deepseek-r1:70b), general (gemma2, mistral-nemo, phi3, gpt-oss) Next Steps:

1. Install missing Python packages: pip3 install fastapi uvicorn
2. Start core DAGI services: Router, Memory, DevTools
3. Fix Qdrant unhealthy status (check logs: docker logs qdrant-vector-db)
4. Configure node-to-node communication with Node #1
5. Set up PostgreSQL for DAARION core memory
6. Test new models with DAGI Router (configure routing for different models)