tpu-gpu
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TPU and GPU are both specialized hardware accelerators used for machine learning workloads, but there are a few key differences: GPU (Graphics Processing Unit): Originally designed for graphics and gaming, but works well for ML due to its parallel architecture. Produced by companies like NVIDIA, AMD, etc. Examples are NVIDIA Tesla V100, RTX 2080, etc. Typically has higher single-precision performance compared to TPU, so works better for models with a lot of single-precision math (e.g. computer vision models). More flexible and can run a wider range of ML frameworks (TensorFlow, PyTorch, MXNet, etc.) and non-ML workloads. TPU (Tensor Processing Unit): Designed specifically for machine learning by Google. Examples are TPU v2 and v3. Has higher performance for low-precision math (e.g. matrix multiplications with 8-bit integers) compared to GPU. This is good for models with high volume of parameter updates (e.g. large language models). Tightly integrated with TensorFlow and works best for models built with TensorFlow. Less flexible support for other ML frameworks or non-ML workloads. Typically available as a cloud service through Google Cloud TPUs. On-premises TPUs also available but more difficult to set up. So in summary: Use GPU if: You need high single-precision performance You need flexibility to run different ML frameworks or non-ML workloads You want an on-premises solution Use TPU if: Your models do a lot of low-precision math (e.g. large neural networks) You are using TensorFlow You want to leverage the TPUs as a cloud service |