TPU vs GPU: Choosing the Right Hardware for Your AI Projects

The AI development surge has increased computing demands, driving the need for robust hardware solutions. A startup developing AI video tools might aim to upscale low-resolution footage to 4K quality in real-time. Specialized hardware would make this possible by processing the complex neural networks required for intelligent frame prediction and detail generation at speeds that traditional CPUs simply cannot achieve. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) have emerged as essential technologies in addressing these demands.

Over time, GPUs have transformed from specialized chips designed solely for rendering video game graphics into versatile processors capable of handling AI tasks efficiently due to their parallel processing capabilities. In 2016, Google introduced TPUs as purpose-built application-specific integrated circuits designed exclusively for neural network processing, featuring dedicated matrix multiplication hardware to accelerate their machine learning workloads.

Read on to explore the key differences between these technologies, understand their architectural strengths, and learn which solution best fits your specific AI workloads and budget constraints.

What is a GPU?

A Graphics Processing Unit (GPU) is a specifically designed processor that manages complex graphical and parallel processing tasks, including image rendering and AI-ML workloads. These were initially designed to render complex 3D graphics for gaming and visual applications. However, their highly parallel architecture, consisting of thousands of small cores optimized for simultaneous operations, was suitable for matrix multiplication and vector operations that underpin modern deep learning algorithms.

GPUs are versatile and support various applications, including scientific computing, graphics rendering, and video processing.

What is a TPU?

Google designed a Tensor Processing Unit (TPU) to offer purpose-built solutions to AI computation needs. Unlike GPUs, which evolved from graphics rendering to AI applications, TPUs were built for neural network operations. These units enhance and optimize TensorFlow and JAX workloads and machine learning tasks, focusing mainly on AI computation, matrix multiplication, and convolution operations.

TPUs have evolved, with each new version offering significant improvements and updates, making workload management effective. They are available via the Google Cloud Platform or Google Colab.

TPU vs GPU: Differences

Both TPUs and GPUs excel at processing AI workloads. However, the difference lies in their approach to managing these workloads and the challenges associated with different architectures influencing their performance, characteristics, and use cases.

GPU architecture

GPUs work with parallelization as their fundamental principle, featuring thousands of small cores designed to handle multiple workloads and tasks simultaneously. Some of the basic components of the architecture include:

• Streaming multiprocessors with multiple CUDA cores

• Specialized tensor cores for accelerating matrix operations

• A high bandwidth memory for rapid data access

• Complex cache hierarchies for effective data management

• Multi-level memory management system

• A flexible programming model

All these components make GPUs highly versatile, suitable for graphics rendering and video processing, and supporting a wide range of AI applications.

TPU architecture

TPUs have a highly specialized architecture optimized for machine learning workloads. This allows TPUs to achieve high performance and efficiency for managing different workloads, although they have limited flexibility compared to GPUs. Some of the significant components of TPU architecture are:

• Vector processing units (VPU) for efficient vector calculations

• Matrix multiplication units designed for tensor operations

• A systolic architecture array for efficient matrix multiplication

• High memory bandwidth positioned for AI workloads

• Deep integration with Google’s TensorFlow and JAX framework

GPU use cases

GPUs have become essential for businesses in AI development across various industries due to their flexibility and ease of availability. Below are some of the most common use cases of GPUs:

Graphics rendering and cloud gaming

GPUs were initially designed to render complex 3D images in real time and excel at parallel visual data processing. They offer a smooth gaming experience with high frame rates and realistic lighting effects. Modern-day gaming engines rely heavily on GPU capabilities for additional features such as ray tracing, physics simulations, and rendering open-world environments with minimal latency.

Deep learning and AI model training

GPUs offer versatility for AI development across multiple frameworks (PyTorch, TensorFlow, and JAX). Due to their features, including wide availability across mature software ecosystems, GPUs are a default choice for most industries and AI researchers. It also supports different precision formats, including FP32, FP16, and INT8, offering flexibility for training and inference optimizations.

3D animation rendering

GPUs are used in film, education, architecture, gaming, and advertising industries to accelerate animation and image rendering time. Effect processing, color grading, timeline scrubbing, and exporting operations all benefit from GPU acceleration. For 3D animation rendering, GPUs reduce the rendering time, especially for complex scenes with advanced lighting, textures, and particle effects.

Computational research

GPUs accelerate complex simulations in industries such as astronomical modeling, molecular dynamics, fluid dynamics, and weather prediction. Parallel processing allows efficient handling of massive datasets and effective simulation running. Research institutions leverage GPU clusters to solve computationally intensive problems on traditional CPU systems that would take months or years, enabling breakthroughs in climate science and pharmaceutical development.

Computer vision applications

Due to their architecture, GPUs power real-time object detection, video analysis, face recognition, and autonomous driving systems. They are well-suited for matrix operations required for processing visual data through convolutional neural networks. Edge computing devices often integrate GPUs to enable on-device computer vision without dependence on the cloud.

TPU use cases

TPUs are more specialized and offer exceptional performance for specific machine learning tasks, especially within the Google ecosystem. Below are some everyday use cases of TPUs :

Image recognition and classification

TPUs’ architecture is optimized for tensor optimization, making them efficient for computer vision models with large datasets. Satellite imagery, medical imaging, and retail inventory systems use TPUs to train on millions of high-throughput images. TPUs also have a deterministic execution model, which provides consistency benefits for regulated applications requiring reproducible results.

Recommendation systems

Online platforms can use TPUs to power recommendation engines that process billions of user interactions. These engines also handle the high-dimensional, sparse matrices standard in collaborative filtering and embedding-based recommendation systems. The high-memory bandwidth supports efficient processing of embedding lookups.

Training of a large language model

TPUs offer the computational power required for the most advanced language models with billions of different parameters. Organizations working with foundation models benefit from using TPUs due to efficient handling of attention mechanisms and other transformer operations. Specific languages used by Google, including BERT, T5, and PaLM, are using TPU infrastructure.

Tensor-flow-based research

Academic and industrial research teams use TPUs to accelerate experimental cycles when working with TensorFlow models. The XLA (Accelerated Linear Algebra) compiler optimizes operations specifically for TPU hardware. Research areas such as generative models, reinforcement learning, and multimodal AI benefit from TPUs’ computational efficiency for large-scale experiments.

ML training in Google Cloud

TPUs in Google Cloud accelerate ML training with specialized hardware optimized for deep learning workloads. They excel at matrix computations required by frameworks like TensorFlow and JAX, enabling efficient training of complex neural networks. Cloud TPUs provide on-demand access to high-performance computing without hardware investment, allowing organizations to scale training jobs across multiple interconnected chips for faster results.

TPU vs GPU FAQ

What is the main difference between TPU and GPU?

Google custom-designs TPUs for machine learning workloads. These use a systolic array architecture optimized for matrix operations, which is common in neural networks. In contrast, GPUs were initially designed for graphics rendering but have been adapted for parallel computing.

Is TPU better than GPU for deep learning?

TPUs efficiently learn tasks involving large batch sizes and models using TensorFlow. GPUs, on the other hand, offer flexibility and a wide software support. As per your workload and specification, you can choose from either of these; however, DigitalOcean’s GPU Droplets provide high performance and enable you to scale your business without worrying about a huge investment.

Are TPUs faster than GPUs?

For specific machine learning workloads, especially optimized for TensorFlow, TPUs can offer 15- 30x better performance per watt than contemporary GPUs. Performance varies according to the workload, model architecture, batch size, and optimization level.

Why does Google use TPUs instead of GPUs?

Google developed TPUs to meet its specific need for higher efficiency in AI computation. These chips help reduce its data center power requirements and offer hardware control for its AI infrastructure.

Can I use a TPU for gaming?

TPUs are designed for machine learning tasks and do not excel at gaming workloads. Instead of TPUs, you can use GPUs, which are highly efficient and have been designed for gaming purposes.

How do TPU costs compare to GPU costs?

With DigitalOcean’s GPU Droplets, the cost is comparatively lower, and they offer higher efficiency, high performance, and flexible configurations; however, for a TPU, these costs can range from $1.35 -$8 per hour, depending on the version.

Which is better for cloud AI training: TPU or GPU?

TPUs work efficiently for TensorFlow models with large batch sizes, while GPUs are preferred for other frameworks, including PyTorch and other small workloads.

References

• PyTorch 101, Understanding Graphs, Automatic Differentiation, and Autograd

• How To Install TensorFlow on Ubuntu 20.04

• Writing CNNs from Scratch in PyTorch

• NumPy Matrix Multiplication

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