GPU

GPU acceleration support in Java requires extra setup and tuning compared to Python, and forcing GPU allocation per application instance (even when idle) creates scaling and maintenance challenges with higher infrastructure costs and lower resource efficiency.

Enterprise architects report difficulties scaling Hugging Face models across multiple GPUs, limiting the platform's applicability for large-scale production deployments.

TensorFlow attempts to allocate all available GPU memory on startup, which can prevent other code from accessing the same hardware and limits flexibility in local development environments where developers want to allocate portions of GPU to different tasks.

When scaling TensorFlow projects on cloud platforms with high-cost GPU configurations, training time grows exponentially, forcing developers to either optimize algorithms or migrate infrastructure, leading to significant cost and complexity issues.

TensorFlow only supports NVIDIA GPUs and Python for GPU programming with no additional support for other accelerators, limiting cross-platform development flexibility.