With the rise of artificial intelligence, the requirement for higher-performance hardware accelerators that can support complex computations has also grown. Among the useful ones are Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). Each has its strengths, and knowledge of these differences is key to helping you choose the best fit for your specific use case.
This article keeps you updated on the performance, architecture, use cases, and cost of GPUs and TPUs to help you decide which technology is best suited for your needs in AI.
The main objective of the GPUs was to perform graphics rendering, essentially for video games. Because they can be processed in parallel, they have been widely utilized in deep learning.
A GPU possesses thousands of cores, supporting hundreds of tasks simultaneously, which explains why they are perfectly suited for throughput-oriented operations, such as matrix multiplication and convolution used in deep learning.
Parallel Processing: Run many computations in parallel, making many tasks such as training large neural networks much faster.
Flexibility: They are general-purpose processors, which means they could do a wide variety of applications beyond AI, including games and video rendering.
Mature Ecosystem: NVIDIA is the market leader for GPU manufacturing and offers libraries like CUDA and cuDNN, which are optimized for AI and machine learning workloads.
Flexibility: GPUs use any type of application, whether it is AI or non-AI.
High Accuracy: GPUs are true computation performers with high accuracy in computations, so it is very useful for training models where precision is everything.
Widely adopted by Deep Learning Community: GPUs are highly adopted by the deep learning community because of their strong performance and compatibility with most machine learning frameworks like TensorFlow and PyTorch.
Power Consumption: The drawing power of a GPU is enormous. This can sometimes contribute to the increased cost of running.
Cost: High-end GPUs such as the NVIDIA A100 are very costly. This automatically makes the GPU impossible for small-scale businesses.
Tensor Processing Units, or TPUs for short, were created by Google as accelerators for machine learning tasks, most notably deep learning models. Even though there isn't an analog for a GPU-specific family, the TPUs are fully conceived and optimized to accelerate tensor-based operations and can be combined with Google's TensorFlow framework. That is why TPUs are widely used for cloud infrastructure.
The TPUs are designed specifically for particular kinds of computations that AI does, which include matrix operations in the neural networks. They are very fast for tasks like training and inference of models of AI.
Energy Efficiency: Generally, the TPUs are more energy-efficient compared to the GPUs. They offer high performance but use lower power; therefore, they consume energy relatively less than the GPUs.
Cloud Integration: Google offers TPUs through its cloud services. Hence, the developer does not need to invest in any kind of infrastructure. Benefits of Using TPUs:
High Speed: Because of specialized hardware, TPUs are very efficient at both training and inference tasks. For massive models like BERT or GPT, the use of TPUs may significantly decrease the training time.
Optimized for TensorFlow: TPUs work great with TensorFlow and, thus, TPUs are excellent choices for projects that are built on TensorFlow.
Energy Efficiency: TPUs need significantly less power than GPUs, thus making them a much more green choice for large-scale operations.
Less Versatile: TPUs are specialized highly, meaning that it is not as flexible as the GPU when it comes to non-AI workloads.
Steeper Learning Curve: Those developers who are accustomed to using GPUs may be faced with a steeper learning curve to getting familiar with the TPU, especially when using other frameworks apart from TensorFlow.
Less Maturated Ecosystem: Google's TPU ecosystem is not so mature. However, it lacks a huge number of libraries and community support like that of a GPU.
Regarding the performance issue, GPUs and TPUs both have good and bad sides. Because GPUs are for general purposes, their performance is much better when needing a wider range of computations. TPUs are simply superb performers for very specific deep learning tasks, particularly in case one is dealing with enormous datasets and models.
Training Speed: In general, TPUs win at training large models such as NLP and computer vision models. Of course, the gap heavily depends on the type of model and much more on the size of the dataset.
Inference: In inference, the same runs faster on TPUs, quite especially when deployed at scale in cloud environments. Compared to GPUs, it is slower, though they allow flexibility using any kind of application.
The best fit for GPUs is
Small to medium-sized AI project workflows that require flexibility
A variety of machine learning frameworks such as PyTorch or TensorFlow
Applications beyond AI, video rendering, and gaming
TPUs are best suited to
Large-scale AI project work using enormous data and complex models
TensorFlow users who want to optimize their workflow
Companies that need to host cloud-based solutions to train their AI models or run them at scale.
Costs
However, this again is at the cost of the choice: whether it is GPUs or TPUs. High-end GPUs are resource-intensive not only in hardware cost but also in operational costs, including power consumption.
TPUs, available only through Google Cloud, keep large-scale training tasks relatively more affordable due to their efficiency. However, it is accessible exclusively as a cloud-based product, which might not suit organizations that have, as a preference, infrastructures installed onsite.
Thus, the choice between a GPU or TPU will depend on what particular needs your AI project has. If one requires flexibility because maybe one works in several frameworks or with small datasets, a GPU might be more convenient due to its more mature and flexible nature.
However, large-scale deep learning projects, especially those revolving around TensorFlow, TPUs will easily accelerate operations at a lower cost of operational feasibility. The best processor will depend on the specific use, budget, and technical requirements.