What are the main advantages of using GPUs over TPUs

1. Versatility: GPUs are highly versatile and can handle a wide range of tasks beyond AI, including graphics rendering, scientific simulations, and financial modeling. This versatility makes them suitable for various applications, not just machine learning[1][3][6].

2. Mature Ecosystem: GPUs have a more mature ecosystem with extensive support from popular frameworks like TensorFlow, PyTorch, and Caffe. This widespread adoption means there are more tools and software available for developers, making it easier to integrate GPUs into different projects[5][7].

3. Precision Options: GPUs offer a range of precision options, from low-precision FP16 to high-precision FP64, which makes them suitable for various workloads with different accuracy requirements. This flexibility is particularly beneficial in applications where precision is critical[5].

4. Flexibility in Customization: GPUs generally provide more customization options compared to TPUs, which can be beneficial in research environments or when adapting to new AI tasks. This flexibility allows developers to optimize GPUs for specific tasks beyond their standard capabilities[5].

5. Compatibility: While TPUs are primarily optimized for TensorFlow and JAX, GPUs can be used with a broader range of frameworks, making them more compatible with diverse software environments[3][5].

However, it's worth noting that GPUs typically consume more power than TPUs and can be more expensive, especially for high-performance models[5][6].