maximum-performance

When it comes to maximum performance for machine learning workloads, GPUs currently have some advantages over TPUs:

Higher single-precision performance: GPUs like the NVIDIA V100 have maximum single-precision (FP32) performance of over 100 teraflops, compared to about 30-60 teraflops for current-gen TPUs. This means GPUs can train single-precision models faster, especially those with a lot of tensor operations that don't quantize well to lower precisions. Examples include some computer vision and natural language models.

Larger memory capacity: High-end GPUs typically have 16-32 gigabytes of onboard memory, compared to about 8-16 gigabytes for TPUs. This larger memory allows GPUs to train models with huge numbers of high-dimensional tensors in FP32 precision without swapping to host memory. Some very large language models may benefit from this.

Faster interconnect: GPU servers are connected with high-speed NVLink interconnects which provide up to 100 gigabytes/second of bandwidth between GPUs. This fast interconnect allows parallel training of large models across multiple GPUs with minimal performance hit. TPU servers currently have standard network interconnects with more limited bandwidth between chips.

Mature software stack: Frameworks like TensorFlow, PyTorch and MXNet all have GPU support and provide tools to facilitate multi-GPU and distributed training. The software tooling to leverage TPUs, especially for non-TensorFlow frameworks, is not as mature which can impact performance.

However, TPUs also have some advantages for maximum performance:

Higher low-precision performance: TPUs far outperform GPUs for 8-bit integer ops and other lower-precision math, with up to 500-1000 teraflops on newer TPU models. This enables faster training of highly quantized models like large language models.

TensorStream architecture: TPUs have a customized data-parallel architecture called TensorStream that is optimized specifically for ML. It minimizes the performance hit from communication between cores, enabling almost linear performance scaling across thousands of cores. Scaling GPUs to that size requires more complex model parallelism due to limitations of data parallelism.

Sparsed TPU array: TPU v3 pods contain up to 2048 TPU cores with a high-bandwidth switched network. This sparse arrangement is more space and power efficient than a dense array of GPUs at scale.

So in summary, while GPUs currently have some maximum performance benefits, TPUs are far superior for high-throughput low-precision computation and also have advantages for extremely large scale. The gap between GPUs and TPUs continues to narrow with each new generation though, so the choice largely depends on how their respective strengths match with your specific ML needs and workloads. Let me know if you have any other questions!