@llm-dev-ops/llm-orchestrator-darwin-arm64

📦 What is this package?

This package contains the pre-compiled native binary of LLM Orchestrator specifically built for macOS systems with Apple Silicon processors (M1/M2/M3/M4). It is automatically installed as an optional dependency when you install @llm-dev-ops/llm-orchestrator on an Apple Silicon Mac.

Why Apple Silicon?

Apple Silicon offers:

• 🚀 Superior Performance - Up to 2x faster than Intel equivalents
• 🔋 Energy Efficiency - Exceptional performance per watt
• 💾 Unified Memory - Faster memory access and lower latency
• 🎯 Native Execution - No Rosetta translation overhead

🚀 Installation

Automatic (Recommended)

Simply install the main package - this platform package will be installed automatically:

npm install -g @llm-dev-ops/llm-orchestrator

Manual (Advanced)

If you need to install this package directly:

npm install @llm-dev-ops/llm-orchestrator-darwin-arm64

Force ARM64 Installation

If you're on Apple Silicon but getting the x64 version:

arch -arm64 npm install -g @llm-dev-ops/llm-orchestrator

⚙️ System Requirements

| Requirement | Minimum | Recommended | |------------|---------|-------------| | OS | macOS 11.0 (Big Sur) | macOS 14+ (Sonoma) | | Chip | Apple M1 | Apple M2 Pro/Max/Ultra, M3/M4 | | RAM | 8 GB | 16 GB+ (32 GB for heavy workloads) | | Disk Space | 50 MB | 100 MB | | Architecture | ARM64 (aarch64) | ARM64 |

Compatible Devices

• ✅ MacBook Air - M1, M2, M3 (2020-2024)
• ✅ MacBook Pro - M1, M1 Pro, M1 Max, M2, M2 Pro, M2 Max, M3, M3 Pro, M3 Max (2020-2024)
• ✅ Mac mini - M1, M2, M2 Pro (2020-2024)
• ✅ Mac Studio - M1 Max, M1 Ultra, M2 Max, M2 Ultra (2022-2024)
• ✅ iMac - M1, M3 (2021-2024)
• ✅ Mac Pro - M2 Ultra (2023+)

🔍 Check Your Mac's Chip

Verify you have an Apple Silicon Mac:

# Check processor type
uname -m
# Apple Silicon outputs: arm64

# Check chip details
sysctl -n machdep.cpu.brand_string
# Shows: "Apple M1/M2/M3/M4..."

# Alternative: Check architecture
arch
# Shows: arm64

Or via System Information:

1. Click Apple menu () → About This Mac
2. Look for Chip: Should show "Apple M1/M2/M3/M4"

🎯 Use Cases

Perfect for:

• Development Machines - MacBook Pro/Air with Apple Silicon
• Production Workloads - Mac Studio, Mac Pro
• CI/CD - macOS ARM64 runners (GitHub Actions, GitLab CI)
• High-Performance Computing - Mac Studio with M1/M2 Ultra
• Local AI Development - Leveraging unified memory architecture

📊 Package Contents

@llm-dev-ops/llm-orchestrator-darwin-arm64/
├── bin/
│   └── llm-orchestrator          # Native ARM64 binary (~12 MB)
├── package.json
└── README.md

🔧 Verification

Verify the installation and check the binary:

# Check installation
npm list -g @llm-dev-ops/llm-orchestrator-darwin-arm64

# Verify binary architecture
file $(which llm-orchestrator)
# Output: Mach-O 64-bit executable arm64

# Check version
llm-orchestrator --version

# Verify native execution (not Rosetta)
sysctl sysctl.proc_translated
# 0 = Native ARM64, 1 = Running via Rosetta

# Check CPU
uname -m
# Output: arm64

⚡ Performance Optimization

Memory Configuration

Apple Silicon's unified memory architecture provides significant performance benefits:

# Check available memory
sysctl hw.memsize

# For large workflows, ensure adequate memory:
# M1/M2: 16+ GB recommended
# M1/M2 Pro: 32 GB recommended
# M1/M2 Max/Ultra: 64+ GB for heavy workloads

Concurrency Settings

Apple Silicon excels at parallel workloads:

# Check CPU cores
sysctl hw.ncpu

# M1: 8 cores (4 performance, 4 efficiency)
# M2: 8 cores (4 performance, 4 efficiency)
# M1 Pro: 8-10 cores
# M1 Max: 10 cores
# M2 Pro: 10-12 cores
# M2 Max: 12 cores
# M1 Ultra: 20 cores
# M2 Ultra: 24 cores

# Optimize concurrency for your chip
llm-orchestrator run workflow.yaml --max-concurrency $(sysctl -n hw.ncpu)

Energy Efficiency

# Monitor energy usage
sudo powermetrics --samplers cpu_power -i 1000

# Apple Silicon provides excellent performance per watt
# Ideal for long-running LLM workflows

🚨 Security Note

Gatekeeper & Notarization

The binary is signed and notarized for macOS Gatekeeper. If you encounter security warnings:

# Clear quarantine attribute
xattr -d com.apple.quarantine $(which llm-orchestrator)

# Verify code signature
codesign -dv $(which llm-orchestrator)

# Check notarization
spctl -a -v $(which llm-orchestrator)

📚 Documentation

For complete usage documentation, see the main package:

• Main Package: @llm-dev-ops/llm-orchestrator
• GitHub Repository: llm-orchestrator
• API Documentation: docs.rs

🐛 Troubleshooting

This is a macOS Gatekeeper warning. To bypass:

# Option 1: Remove quarantine attribute
xattr -d com.apple.quarantine $(which llm-orchestrator)

# Option 2: Allow in System Settings
# System Settings → Privacy & Security
# Scroll down to Security section
# Click "Allow Anyway" next to the blocked message

Ensure you're installing the ARM64 version:

# Check current architecture
arch
# Should show: arm64

# Force ARM64 installation
arch -arm64 npm uninstall -g @llm-dev-ops/llm-orchestrator
arch -arm64 npm install -g @llm-dev-ops/llm-orchestrator

# Verify native execution
sysctl sysctl.proc_translated
# Should show: 0 (native)

Ensure npm's bin directory is in your PATH:

# Add to ~/.zshrc
echo 'export PATH="$(npm bin -g):$PATH"' >> ~/.zshrc

# Reload shell
source ~/.zshrc

Ensure Xcode Command Line Tools are installed:

xcode-select --install

# Verify installation
xcode-select -p
# Should show: /Library/Developer/CommandLineTools

💡 Pro Tips

Optimize for Apple Neural Engine

While this binary doesn't directly use the Neural Engine, you can optimize workflows:

# Use local models when possible to leverage ANE
# Configure faster models for simple tasks
providers:
  openai:
    type: openai
    model: gpt-3.5-turbo  # Faster for simple tasks

  claude:
    type: anthropic
    model: claude-3-haiku-20240307  # Cost-effective for analysis

Leverage Unified Memory

# For workflows with large context windows
# Apple Silicon's unified memory excels here

# M1 Max (64GB): Handle very large contexts
# M1 Ultra (128GB): Process multiple large workflows concurrently

🔗 Related Packages

| Package | Platform | npm | |---------|----------|-----| | Main Package | All platforms | @llm-dev-ops/llm-orchestrator | | Linux x64 | Linux x64 | @llm-dev-ops/llm-orchestrator-linux-x64 | | Linux ARM64 | Linux ARM64 | @llm-dev-ops/llm-orchestrator-linux-arm64 | | macOS Intel | macOS x64 | @llm-dev-ops/llm-orchestrator-darwin-x64 |

📄 License

MIT OR Apache-2.0

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Part of the LLM Orchestrator project