Journey to GPU-Powered Deep Learning: Installing TensorFlow on Windows 11 with RTX 3080

Why We Did This

Like many AI enthusiasts, I wanted to leverage the raw power of my RTX 3080 to train deep learning models — LSTM Transformers — all on GPU. The dream? Lightning-fast model training, smooth parallelism, and a rock-solid development environment. The challenge? Installing TensorFlow with GPU support on Windows 11 — without using Conda. Let me walk you through what actually happened.

The Struggles We Faced (And You Might Too)

1. Wrong Python Version

Initially, the system had Python 3.11+ installed — but TensorFlow GPU support (up to version 2.10) only works with Python 3.10 or below. Anything beyond that caused weird import issues and incompatibility errors. Fix: We downloaded and installed Python 3.10.0, and made sure to check the box “Add Python to PATH” during install.

2. CUDA Confusion

We needed CUDA 11.2, but NVIDIA’s site only listed it as compatible with Windows 10 — nowhere did it say “Windows 11.” That caused hesitation. Fix: We installed cuda_11.2.0_460.89_win10.exe anyway — and it worked flawlessly on Windows 11.

3. cuDNN Setup Pain

Downloading and installing cuDNN 8.1 took some effort — registering with NVIDIA, locating the exact version, unzipping, and copying bin, include, and lib files to the CUDA directory manually. If any step was missed, TensorFlow wouldn’t detect GPU. Fix: We carefully copied the cuDNN files into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\

4. TensorFlow Import Errors

After finally installing TensorFlow, we hit a critical wall: AttributeError: ARRAYAPI not found ImportError: numpy.core._multiarray_umath failed to import These errors were triggered because NumPy 2.x was incompatible with TensorFlow 2.10, which was compiled using NumPy 1.x. Fix: We downgraded NumPy: pip install numpy==1.23.5

The Breakthrough: Success Message

We ran: import tensorflow as tf print("TF:", tf.__version__) print("GPUs:", tf.config.list_physical_devices('GPU')) And finally saw: TF: 2.10.0 GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] Built with CUDA: True Built with cuDNN: True At that moment, the GPU usage graph spiked — proof that TensorFlow and Keras models were using RTX 3080 exactly as planned.

What We Learned

- TensorFlow GPU setup on Windows is very doable, but version matching is everything. - Avoid latest NumPy or Python unless you're using TensorFlow Nightly or building from source. - Even without Conda, a pip-based setup works great when you're careful.

Final Setup Snapshot

Component      | Version ---------------|------------------ Python         | 3.10.0 TensorFlow     | 2.10.0 (GPU) CUDA Toolkit   | 11.2 cuDNN          | 8.1 NumPy          | 1.23.5 GPU            | RTX 3080 (10GB VRAM) OS             | Windows 11

What’s Next?

We’re now running 3 models in parallel per batch, training deep learning models for project  using: - LSTM And all of it, on one RTX 3080 GPU — efficiently and reliably.