Table of contents
What is a GPU?What is a TPU?Key differencesWhich workloads benefit mostWhich workloads are better suitedReal-world considerationsTPU vs GPU for LLMsSummaryFAQsNext stepsAdditional resources
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GPU → vs TPU

TPU vs GPU: A beginner’s guide

Choosing the right hardware for AI and high-performance computing (HPC) can feel overwhelming, especially when terms like GPU and TPU start flying around. Both are powerful chips designed to accelerate processing, but they serve different purposes—and picking the wrong one could mean wasted resources or underwhelming performance.

Whether you’re training machine learning models, running AI-powered applications, or optimizing workloads in the cloud, understanding how these processors stack up is key. Let’s break down the differences so you can make the right call for your needs.

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What is a GPU?

A GPU is a Graphics Processing Unit, originally designed to handle rendering tasks for video games and 3D applications. What makes GPUs powerful is their architecture: thousands of small cores designed for parallel computing.

That parallelism translates extremely well to machine learning. Instead of graphics, many developers now use GPUs to accelerate matrix operations, convolutional layers, and backpropagation.

Common GPU use cases:

  • Gaming and visual effects
  • Deep learning model training (e.g., CNNs, LLMs)
  • Scientific simulations
  • Video rendering and image processing

NVIDIA dominates the ML/AI space with its CUDA platform and Tensor Cores, though AMD and Intel also manufacture GPUs for other high-performance computing tasks.

What is a TPU?

A TPU is a Tensor Processing Unit, custom-designed by Google specifically for deep learning. Unlike general-purpose GPUs, TPUs are application-specific integrated circuits (ASICs): hardware tuned for one job: matrix math for neural networks.

TPUs are designed to crank through massive volumes of tensor operations, which is ideal for training and inference with large-scale models. However, they’re optimized primarily for TensorFlow, Google’s machine learning framework.

Where TPUs are used:

  • Google Cloud AI Platform
  • YouTube video recommendations
  • Google Search and Translate
  • Training large language models like PaLM and Gemini

You won’t find TPUs in personal computers or local hardware; they’re mostly available through Google Cloud or Google Colab.

TPU vs GPU: Key differences

Let’s break down how these accelerators compare when it comes to core capabilities, performance, cost, and use cases.

Specialization

  • TPUs: Purpose-built for deep learning workloads, especially with TensorFlow
  • GPUs: Versatile—work across TensorFlow, PyTorch, JAX, and more

Performance

  • TPUs: Extremely fast for dense matrix operations, ideal for large batch sizes
  • GPUs: Competitive performance with better support for dynamic computation graphs and real-time debugging

Flexibility

  • GPUs support a wider ecosystem of frameworks and libraries
  • TPUs require specific model formatting and are more rigid in terms of developer tooling

Deployment options

  • GPUs: Available in cloud, bare metal servers, consumer desktops, and workstations
  • TPUs: Only accessible through Google services (e.g. Cloud or Colab)

Cost considerations

  • TPUs can offer better price/performance at scale, but you’re locked into Google Cloud pricing
  • GPUs are available for rent (dedicated servers, cloud GPUs) or for purchase, giving more control over costs

Which workloads benefit most from TPUs?

TPUs are a smart choice when you’re:

  • Using TensorFlow exclusively
  • Training very large models that require high-throughput computation
  • Running batch-heavy deep learning tasks
  • Deploying inference pipelines for large-scale production models on Google Cloud

Just make sure your architecture is compatible. TPUs struggle with models that use dynamic shapes, custom ops, or require advanced debugging.

Which workloads are better suited for GPUs?

GPUs are the go-to for:

  • Experimentation and prototyping (e.g. local training on PyTorch or JAX)
  • Multi-purpose environments that include rendering or scientific computing
  • Dynamic models with control flow or conditional logic
  • Teams who want full access to drivers, custom kernels, and extensive community support

Plus, if you want to train models on-prem or in a dedicated server without relying on Google’s ecosystem, GPUs are your only real option.

Real-world considerations when choosing between GPU and TPU

Ecosystem and tooling

GPUs support a broader range of ML libraries, container images, and orchestration platforms (like Docker and Kubernetes). They’re also easier to debug thanks to mature developer tools like PyTorch Profiler, NVIDIA Nsight, and CUDA debugger.

Developer experience

Most tutorials, courses, and GitHub repos are GPU-focused. If you’re just getting started, you’ll likely have an easier time ramping up with a GPU-based stack.

Compatibility and deployment

Want to deploy to AWS, Azure, or on a dedicated server? That’s a GPU world. TPUs are tied tightly to Google Cloud’s infrastructure.

Community and support

GPU workflows are far more common across the ML landscape. You’ll find more prebuilt Docker images, pretrained models, Stack Overflow answers, and vendor support.

TPU vs GPU for LLMs

Large language models push hardware to the edge, and both TPUs and GPUs are built to handle them—but not equally.

TPUs in LLMs

Google uses TPUs to train and deploy massive models like Gemini and PaLM. These models are trained on vast TPU pods designed to maximize throughput and minimize latency, especially during inference.

GPUs in LLMs

Most commercial and open-source LLMs—like GPT-4, LLaMA, and Claude—run on NVIDIA H100 or A100 GPUs. GPU stacks offer more:

  • Flexibility across frameworks (especially PyTorch)
  • Compatibility with parallelization tools (e.g. DeepSpeed, Megatron-LM)
  • Control over memory allocation and scaling

If you’re training or fine-tuning LLMs outside of Google’s ecosystem, GPUs are the industry standard.

Summary: Choosing the right accelerator

Pick TPUs if:

  • You’re training large TensorFlow models.
  • You’re already deep in the Google Cloud ecosystem.
  • You need max throughput and don’t require much debugging flexibility.

Pick GPUs if:

  • You use multiple frameworks (PyTorch, TensorFlow, JAX).
  • You’re experimenting, debugging, or building across environments.
  • You want to own or rent hardware, not be locked into a single provider.

TPU vs GPU FAQs

It depends. TPUs outperform GPUs on some TensorFlow-specific tasks at scale. But GPUs are far more flexible, better supported across frameworks, and available in a wider variety of configurations.

ChatGPT runs on GPUs—typically high-end NVIDIA A100s or H100s. These GPUs are optimized for large transformer models and support the frameworks used in OpenAI’s infrastructure.

In most cases, yes. GPUs offer better ecosystem support for training and fine-tuning large language models, especially when using PyTorch, DeepSpeed, or distributed inference.

It can be, but only for certain models. If you’re training a large TensorFlow model with compatible architecture, TPUs can outperform GPUs. But for general use or smaller-scale tasks, GPUs are usually faster and easier to work with on Colab.

Additional resources

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Amy Moruzzi is a Systems Engineer at Liquid Web with years of experience maintaining large fleets of servers in a wide variety of areas—including system management, deployment, maintenance, clustering, virtualization, and application level support. She specializes in Linux, but has experience working across the entire stack. Amy also enjoys creating software and tools to automate processes and make customers’ lives easier.