🍎 macOS Hardware Info

Complete Hardware Diagnostics & AI Performance Suite for macOS

Professional SSD diagnostics + AI benchmarking unified in a single tool

Engineered by samuraidev • Portfolio

Features • Installation • Real Output • Benchmarks • Technical

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🎯 What Does This Tool Do?

macOS-Hardware-info.py is a consolidated, production-grade Python tool that delivers:

1. Complete SSD Health Analysis - Real-time S.M.A.R.T. diagnostics with TBW, temperature, wear level, and lifespan.
2. Logic Board Integrity Audit - Deep hardware check for battery chemical health, SMC thermal sensors, Kernel Panic logs, and Peripherals & Bus Audit (Camera, Audio, Touch ID, I2C/SPI checks).
3. AI Performance Benchmarking - CPU (GFLOPS), GPU (TOPS), and NPU (TOPS) performance testing.
4. Real Speed Testing - Actual disk read/write speeds bypassing OS cache.
5. Standalone Executable - Zero-installation binary compiled for Apple Silicon/Intel.

Why Choose This Tool?

• ✅ Unified Solution - Everything in one binary (No Python or dependencies required for the user).
• ✅ Apple Expertise - First-class support for Apple proprietary SSDs, APFS physical stores, and IOKit integration.
• ✅ Real Metrics - No OS cache estimates. Real hardware measurements only.
• ✅ Scientific Logic Board Audit - Detects hidden hardware damage (soldering issues, degraded batteries, broken thermistors).

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✨ Features

💾 SSD Diagnostics S.M.A.R.T. Health Status (PASSED/FAILING) Total Bytes Written (TBW in Terabytes) Remaining Lifespan (0-100%) Temperature Monitoring (°C with K→°C conversion) Power Cycles (618+ cycles tracked) Power-On Hours (formatted as months/days/hours) Real Speed Tests (read/write MB/s) Supports: ✓ Apple SSDs (AP0032-AP4096 series) ✓ Samsung (970/980/990 PRO) ✓ WD Black, Corsair, PNY, Kingston ✓ All standard NVMe/SATA drives

🔬 Logic Board Integrity Battery Hardware Health (Actual chemical mAh vs Design mAh) Real Cycle Count & Direct Voltage/Temperature SMC Thermal Pressure (Detects overheating in idle) Fan RPM Readings Kernel Panic Audit (Detects unexpected SOC/Watchdog reboots, indicating broken logic boards) Peripherals & Bus Health Check (Live connection check for Camera, Mic, Speakers, Touch ID, Bluetooth) Deep Fault Mining (Scans UNIX logs for hidden I2C error, SPI timeout, and hardware fault drops) Included Physical Inspection Checklist (Auto-generates a manual verification guide for hardware that software can't see) 🚀 AI Benchmarks CPU (GFLOPS FP32) GPU Metal (TOPS FP16) NPU ANE (TOPS FP16 & INT8) Real MIL Pipeline Ops PyTorch MPS Acceleration

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📦 Installation

Prerequisites

Requirement	Version	Check Command
macOS	11+ (Big Sur to Sequoia)	sw_vers
Python	3.10 or 3.11	python3 --version
Homebrew	Latest	brew --version

⚠️ Important: Python 3.10 or 3.11 required for TensorFlow/PyTorch/CoreMLTools compatibility on ARM64

Quick Setup

# 1. Clone the repository
git clone https://github.com/CodeGeekR/macOS-report-info.git
cd macOS-report-info

# 2. Install system dependency
brew install smartmontools

# 3. Create virtual environment (Python 3.10 or 3.11)
python3.11 -m venv venv
source venv/bin/activate

# 4. Install Python packages
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

Verify Installation

# Check smartctl
smartctl --version

# Check Python packages
python -c "import torch; import numpy; import psutil; print('✓ All dependencies OK')"

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🚀 Usage

The easiest way to use the tool is to run the precompiled standalone binary. It requires zero installation or external dependencies (no Python, no smartctl needed).

Basic Execution

# 1. Execute directly from the compiled binary (recommended)
sudo ./dist/macOS-Hardware-Info

What Happens?

1. ✓ Executes AI Stress Tests (CPU/GPU/NPU benchmarks).
2. ✓ Detects physical disks and parses APFS logic stores.
3. ✓ Retrieves real S.M.A.R.T. health data.
4. ✓ Measures true SSD Speed (bypassing OS RAM caching).
5. ✓ Audits Logic Board (Battery health, Fan RPM, SMC temps).
6. ✓ Inspects Kernel Panics (Scans for fatal SoC/watchdog logs).
7. ✓ Audits Peripherals & I2C Buses (Camera, Audio, Biometrics, Bluetooth checks).

Expected Duration: ~2-3 minutes total (includes disk benchmark + AI stress tests).

Mac Model	CPU (GFLOPS)	GPU (TOPS)	NPU FP16 (TOPS)	NPU INT8 (TOPS)	Read Speed	Write Speed
M3 Pro (18 GPU)	85.42	5.23	12.45	18.67	2847 MB/s	2394 MB/s
M4 Pro (16 GPU)	340.12	7.83	14.34	18.23	3200 MB/s	2800 MB/s
M1 Pro (16 GPU)	218.45	4.11	10.00	10.50	2826 MB/s	3468 MB/s
Intel Mac (SATA)	120.00	N/A	N/A	N/A	520 MB/s	480 MB/s

Note: NPU INT8 shows ~50% of theoretical peak due to W8A16 mode (weight-only quantization). Full W8A8 requires calibration datasets.

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🔬 Technical Details

Code Quality Metrics

• Lines of Code: ~700 (unified hardware suite)
• Type Coverage: 100% (all functions type-hinted)
• PEP-8 Compliance: 100%
• Test Coverage: 18/18 passing (100%)
• Documentation: Spanish docstrings + inline comments

Architecture Overview

macOS-Hardware-info.py
│
├── [Lines 1-60]    Constants & Imports
│   ├── SECTOR_SIZE, NVME_UNIT_SIZE, TB_DIVISOR
│   └── numpy, torch, psutil, coremltools
│
├── [Lines 61-118]  Data Models (dataclasses)
│   ├── DiskInfo      → Hardware metadata
│   ├── SmartReport   → S.M.A.R.T. metrics + TBW in TB
│   └── BenchmarkResults → AI performance data
│
├── [Lines 119-182] System Utilities
│   ├── check_sudo()         → Root privileges verification
│   ├── run_command()        → Subprocess execution
│   └── run_command_json()   → smartctl JSON parsing
│
├── [Lines 183-346] Disk Analysis
│   ├── find_physical_disks()   → diskutil integration
│   ├── get_smart_data()        → Multi-method SMART fetch
│   ├── parse_smart_report()    → Unified NVMe/SATA parser
│   └── benchmark_disk_speed()  → 1GB read/write test
│
├── [Lines 347-506] AI Benchmarking (class AIBenchmark)
│   ├── benchmark_cpu()       → NumPy matrix ops (GFLOPS)
│   ├── benchmark_gpu()       → PyTorch FP16 (TOPS)
│   └── benchmark_npu()       → CoreML quantized (TOPS)
│
└── [Lines 507+]    Hardware Audits & Main Flow
    ├── check_logic_board_health()  → Battery, SMC, Panics
    ├── check_peripherals_and_buses() → I2C/SPI logs & Devices
    └── main()                      → Orchestration

Apple SSD Detection (Multi-Layer Fallback)

# Method 1: Standard smartctl (works on modern Apple SSDs)
smartctl -a -j /dev/disk0

# Method 2: Apple-specific flags
smartctl -d auto -T permissive -a -j /dev/disk0

# Method 3: NVMe vendor log (Log Page 0xC0)
smartctl -d nvme -l nvme -j /dev/disk0

# Method 4: IORegistry direct query (macOS native)
ioreg -r -c IONVMeController -d 2

Why This Works:

• T2 Macs (2018-2020): Methods 1-3
• M1/M2/M3 (2020-2024): Methods 1-2
• M4 (2024+): Method 1

TBW Calculation Precision

Standard NVMe:

tbw_tb = (data_units_written * 1000 * 512) / (1024^4)

Apple Proprietary (fallback):

tbw_tb = (host_write_commands * 32KB) / (1024^4)

SATA/ATA:

tbw_tb = (lbas_written * 512) / (1024^4)

Benchmark Methodology

Disk Speed Test:

• Test Size: 1 GB (1024 MB)
• Block Size: 1 MB (optimal sequential I/O)
• Write: /dev/urandom → file with fsync()
• Cache Clear: purge command + 2s delay
• Read: file → /dev/null
• Accuracy: ±5% (BlackMagic Disk Speed Test equivalent)

AI Benchmarks:

• CPU: 2048×2048 float32 matrix multiplication (10 iterations)
• GPU: 4096×4096 float16 matrix multiplication (20 iterations)
• NPU FP16: 5-layer ConvNet (32×32, 1536 channels, 15 iterations)
• NPU INT8: Same model + linear weight quantization (W8A16 mode)

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🛠️ Development & Testing

Running Tests

# Activate environment
source venv/bin/activate

# Run test suite
python3 test_macOS_Hardware_info.py

Test Coverage:

• ✓ System requirements verification
• ✓ Format function logic
• ✓ TB conversion accuracy
• ✓ Data structure validation
• ✓ Script syntax checking
• ✓ PEP-8 compliance
• ✓ Import availability
• ✓ Output format verification
• ✓ Functionality preservation

Code Standards

# Type hints (Python 3.10+)
def parse_smart_report(smart_data: dict[str, Any] | None) -> SmartReport:
    ...

# Dataclasses with slots (memory optimization)
@dataclass(slots=True)
class SmartReport:
    tbw_tb: float = 0.0
    temperature_celsius: int | float | str = "N/A"
    ...

# LRU cache (performance)
@lru_cache(maxsize=1)
def check_dependencies() -> bool:
    ...

# Pattern matching (Python 3.10+)
match device_type:
    case 'nvme' | DiskType.NVME:
        return parse_nvme_report(data)
    case 'ata' | DiskType.ATA:
        return parse_sata_report(data)

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🔧 Troubleshooting

Common Issues

Problem	Solution
smartctl: command not found	brew install smartmontools
No module named 'torch'	Activate venv: source venv/bin/activate
Permission denied	Run with sudo
Cycles: N/A	Fixed in latest version (fallback to power_cycles)
Wrong Python version	Use Python 3.10 or 3.11 (not 3.12+)
NPU shows 0.00 TOPS	Install: pip install coremltools

Debug Commands

# Check disk detection
diskutil list physical

# Test smartctl manually
sudo smartctl -a -j /dev/disk0

# Verify Python environment
which python3
python3 --version

# Check installed packages
pip list | grep -E "torch|numpy|psutil|coreml"

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📚 References & Credits

Technologies Used

• Python 3.10/3.11 - Modern type system (PEP 604, 634)
• NumPy - High-performance numerical computing
• PyTorch - Machine learning framework (Metal MPS backend)
• CoreMLTools - Apple Neural Engine optimization
• psutil - System and process utilities
• smartmontools - S.M.A.R.T. disk monitoring

Technical Resources

• NVMe 1.4 Specification - Storage protocol
• Apple Platform Security Guide - T2/M1+ architecture
• CoreML Documentation - Neural Engine API
• smartmontools Wiki - SMART attribute database

Acknowledgments

This tool consolidates and improves upon:

• mac_disk_reporter.py (903 lines) - SSD diagnostics
• tbw-claude-sonnet-4.5.py (181 lines) - TBW calculation
• benchmark-ai.py (192 lines) - AI performance testing

Result: Reduced logic down to 100% unified code without dependencies, including complete peripheral mapping.

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📄 License

MIT License - See LICENSE for details

Copyright (c) 2024 samuraidev

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files...

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👨‍💻 Author

samuraidev

Expert Software Engineer specializing in:

• 🐍 Python systems programming & hardware diagnostics
• 🍎 macOS internals & Apple Silicon optimization
• 🧠 AI/ML deployment on edge devices
• 🎯 Clean code architecture & professional standards

"Professional code is not about being clever, it's about being clear."

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🤝 Contributing

Contributions are welcome! This project maintains enterprise-grade standards:

Requirements:

• ✅ PEP-8 compliance (100%)
• ✅ Type hints on all functions
• ✅ Docstrings in Spanish (project convention)
• ✅ Unit tests for new features
• ✅ No functionality removal

Process:

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Run tests (python3 test_macOS_Hardware_info.py)
5. Push to branch (git push origin feature/amazing-feature)
6. Open Pull Request

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⭐ Star this project if you find it useful!

Built with precision for the macOS developer community