NVIDIA 4070 Ti GPU Benchmark & Setup 🚀

A complete GPU setup and benchmarking workflow for the NVIDIA GeForce RTX 4070 Ti, featuring installation steps, environment configuration, and a TensorFlow-based complex matrix multiplication benchmark.

---

📖 Table of Contents

1. Overview
2. Hardware & Software Requirements
3. Setup Instructions
4. Benchmark Script
5. Sample Results
6. Project Structure
7. Contributing
8. License

---

🔍 Overview

This repository demonstrates how to: - Configure a Python environment with CUDA and cuDNN support.\

• Install TensorFlow GPU and PyTorch GPU for deep learning.\
• Run a stress-test benchmark using large complex matrix multiplications on the NVIDIA 4070 Ti.

The benchmark measures GPU utilization by repeatedly multiplying large complex matrices, providing both total runtime and average iteration time.

---

⚙️ Hardware & Software Requirements

• GPU: NVIDIA GeForce RTX 4070 Ti\
• CUDA Toolkit: 11.2\
• cuDNN: 8.1\
• Python: 3.8+\
• Conda: Recommended for environment management

---

🛠 Setup Instructions

Run the following commands step by step:

# Create a dedicated conda environment
conda create --name jethexa python=3.8 -y
conda activate jethexa

# Install CUDA & cuDNN
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1 -y

# Install TensorFlow GPU
pip install tensorflow-gpu==2.10.1 
pip install keras==2.10.0

# Install PyTorch GPU (CUDA 11.8 compatible)
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Install NVIDIA CUDA compiler tools
conda install -c nvidia cuda-nvcc

# Optional: TensorFlow Addons
pip install tensorflow-addons

---

⚡ Benchmark Script

The benchmark is implemented in gp.py.
It performs 2500 iterations of large-scale complex matrix multiplications and prints iteration times.

Run benchmark:

python gp.py

Script Highlights:

• Detects GPU availability automatically.
• Generates random complex-valued matrices.
• Measures per-iteration time and total runtime.

Example snippet:

gpus = tf.config.list_physical_devices('GPU')
if not gpus:
    print("GPU is not available.")
else:
    print(f"Using GPU: {tf.test.gpu_device_name()}")

---

📊 Sample Results

Iteration Time Taken (s)

---

1 1.42 2 1.39 ... ... 2500 1.41

Total Time: ~3520 seconds
Average per Iteration: ~1.41 seconds

🖼️ Sample GPU Monitoring (replace with your screenshot):

---

📂 Project Structure

nvidia-4070ti-gpu-benchmark/
│── gp.py                # Benchmark script
│── gpu-setup.docx       # Detailed setup instructions
│── README.md            # This file
│── images/              # Screenshots, GPU monitoring graphs

---

🤝 Contributing

Contributions are welcome!\

• Open an issue for feature requests or bug reports.\
• Submit pull requests with improvements.

---

📜 License

This project is licensed under the MIT License.
See LICENSE for details.

---