AI Performance Benchmark Tool

A professional-grade benchmarking suite designed to validate the full AI capabilities of Apple Silicon (M1/M2/M3/M4). It unifies CPU, GPU (Metal), and NPU (Neural Engine) testing into a single tool.

This script implements advanced engineering strategies like Cache-Resident Inference and INT8 Weight Quantization to measure the true potential of the Apple Neural Engine (ANE).

🚀 Key Features

• Full Spectrum Analysis:
  • CPU (NumPy): Measures raw floating-point performance (GFLOPS).
  • GPU (Metal MPS): Tests Compute (FP32) and Inference (FP16) throughput.
  • NPU (CoreML): Utilizes coremltools to bypass Python bottlenecks and access the Neural Engine directly.
• Advanced NPU Strategies:
  • Cache-Resident Model: Uses deep layers with small tensors (32x32) to prevent RAM bottlenecks and saturate the NPU's internal SRAM.
  • INT8 Quantization: Applies linear weight quantization to unlock the Neural Engine's acceleration logic.
• Hardware Detection: Auto-detects Physical/Logical cores, GPU Core count, and NPU driver status.

Prerequisites

• Python 3.10 or 3.11 (Required for TensorFlow/PyTorch compatibility on macOS ARM64).
• Architecture: ARM64 (Apple Silicon) or x86_64.

Installation & Setup

1. Create a clean virtual environment:

   # Verify you are using Python 3.10 or 3.11
   python3.10 -m venv venv
   
   # Activate the environment
   source venv/bin/activate

2. Install dependencies: Note: We strictly require numpy<2 to prevent conflicts with TensorFlow.

   pip install --upgrade pip setuptools wheel
   pip install -r requirements.txt

Usage

Run the script directly from your terminal:

python benchmark-ai.py

Understanding the Output

CPU Baseline (GFLOPS): Standard floating-point performance on the processor.

FP32 (TFLOPS): Raw GPU Compute power. High precision, used for training or scientific calc.

FP16 (TOPS): AI Inference power. Lower precision, faster speed. This metric aligns closer with NPU/Neural Engine marketing specs.

📝 Example Result (Apple M4 Pro)

================================================================================
🚀  AI BENCHMARK PRO
================================================================================
OS: Darwin 24.6.0 | RAM: 24.0 GB
CPU: arm (12 Physical / 12 Logical)
GPU: MPS (16 Cores) | NPU: Enabled
--------------------------------------------------------------------------------
[1] CPU BASELINE (FP32)... 340.12 GFLOPS
[2] GPU METAL (FP16)...... 7.83 TOPS
[3] NPU NEURAL (FP16)..... 14.34 TOPS
[4] NPU NEURAL (INT8)..... 18.23 TOPS

================================================================================
🏆  INFORME TÉCNICO DE RENDIMIENTO
================================================================================
• CPU (General Processing):      340.12 GFLOPS
• GPU (Graphics / Basic AI):     7.83 TOPS
• NPU (High Precision AI):       14.34 TOPS
• NPU (Quantized AI W8A16):      18.23 TOPS
--------------------------------------------------------------------------------
NOTE: The ~18.23 TOPS result represents ~50% of the theoretical peak.
This is the maximum speed achievable without a calibration dataset (W8A16 mode).
To reach full TOPS (W8A8), a real-world trained model with activation
quantization is required.
================================================================================