GPU T4 Vs GPU P100 | Kaggle | GPU (2024)

Most of the people who uses Kaggle don’t know about which GPU should be used for training and inference.

The NVIDIA T4 and P100 GPUs are both data center GPUs designed for different workloads. Here are some of the main differences between the two GPUs:

1. Architecture: The T4 GPU is based on the Turing architecture and is optimized for inference workloads, while the P100 GPU is based on the Pascal architecture and is optimized for both inference and training workloads.
2. Performance: The P100 GPU offers higher performance than the T4 GPU, especially for training workloads. This is due to its larger number of CUDA cores and higher clock speeds.
3. Energy Efficiency: The T4 GPU is more energy-efficient than the P100 GPU, with a lower thermal design power (TDP) of 70 watts compared to 250 watts for the P100.
4. Memory: The P100 GPU has more memory than the T4 GPU, with 16 GB of high-bandwidth memory (HBM2) compared to 16 GB of GDDR6 memory for the T4.
5. Price: The P100 GPU is more expensive than the T4 GPU, reflecting its higher performance and memory capabilities.

In general, the T4 GPU is a good choice for inference workloads that require high throughput and low power consumption, while the P100 GPU is a better choice for training workloads that require high performance and memory capacity.

TPU P100 Should be used for training and for Inference both GPUT P100 for GPUT T4 can be used.