If AI is the new electricity, then chips are the wiring. But not all wires are the same. When people talk about CPUs, GPUs, and TPUs, it can sound like alphabet soup. The good news: you do not need a PhD to understand the basics and make smart decisions.

Think of an AI workload like a kitchen. A CPU is a master chef who can handle many different tasks with finesse. A GPU is a team of line cooks who can grill thousands of identical burgers at once. A TPU is a specialized pizza oven built to crank out one style of dish extremely fast. The right choice depends on what you are cooking and for how many guests.

In this post, you will learn what each chip is best at, how training differs from inference, and how companies behind tools like ChatGPT, Claude, and Gemini use them. By the end, you will be able to match your workload to the right hardware with confidence.

The quick answer: CPUs vs GPUs vs TPUs

  • CPU (Central Processing Unit): Flexible, great at general computing, precise control, lower parallel throughput. Best for mixed workloads, orchestration, and smaller models or low-latency logic.
  • GPU (Graphics Processing Unit): Massively parallel, high throughput for matrix operations. Best for deep learning training and fast batched inference.
  • TPU (Tensor Processing Unit): Purpose-built accelerators (primarily in Google Cloud) optimized for tensor math. Best for large-scale training and inference in TensorFlow/JAX ecosystems with strong price/performance at scale.

If you care about latency and flexibility, start with CPUs. If you need raw parallel math for deep learning, GPUs rule. If you are all-in on Google Cloud and TensorFlow/JAX, TPUs can be a performance and cost win.

What a CPU is good at

A CPU is like a Swiss Army knife: it can do a bit of everything and switch tasks quickly. It has a small number of powerful cores designed for low-latency, sequential logic.

Strengths:

  • Excellent for control flow, branching, preprocessing, and postprocessing
  • Runs most programming languages and legacy code easily
  • Handles small to medium models for low-throughput inference
  • Great for lightweight vector ops using SIMD (e.g., Intel AVX, ARM NEON)

Trade-offs:

  • Limited parallel math compared to GPUs/TPUs
  • Scaling training on CPUs alone is typically inefficient

Real-world example:

  • Serving a retrieval-augmented generation (RAG) workflow: the CPU handles search queries, ranking candidates, text cleaning, and API orchestration, while deferring the heavy matrix math to a GPU.
  • Edge devices (e.g., laptops with Apple M-series) can run smaller models on CPU for offline tasks like summarization, though dedicated accelerators often help.

Why GPUs changed AI

A GPU contains thousands of smaller cores designed to perform the same operation on many pieces of data in parallel. Deep learning is mostly matrix multiplications and convolutions, which fit this model perfectly.

Strengths:

  • High throughput for tensor operations (matrix multiplies)
  • Huge memory bandwidth to feed data to cores
  • Mature software stacks: CUDA, PyTorch, TensorFlow
  • Widely available in clouds and on-prem (NVIDIA H100/H200, A100; AMD MI300)

Trade-offs:

  • Power-hungry and expensive at scale
  • Requires batching for best efficiency (can impact latency)
  • Supply constraints can limit availability

Real-world examples:

  • Training large models: GPT-style models and diffusion models are typically trained on clusters of NVIDIA GPUs. ChatGPT and Claude-like systems originally trained on large GPU fleets.
  • Creative workloads: Stable Diffusion image generation runs fast on a single consumer GPU; many creators use an RTX 4070/4080/4090 for local generation.

What is a TPU and when it shines

A TPU is a Tensor Processing Unit designed by Google to accelerate linear algebra for machine learning, especially TensorFlow and JAX workloads. Instead of general-purpose cores, TPUs use systolic arrays that move data efficiently through specialized circuits.

Strengths:

  • High performance per watt for tensor math
  • Strong scaling for large training jobs (TPU Pods)
  • Tight integration with Google Cloud and tooling

Trade-offs:

  • Primarily available in Google Cloud; limited on-prem options
  • Best results with TensorFlow or JAX; PyTorch support exists but is not as mature
  • Specialized features require some adaptation of code

Real-world examples:

  • Google Search and Google Photos rely on TPUs to power ranking and vision models.
  • Google Gemini models are trained and served across TPU generations (e.g., v4, v5p), taking advantage of cost-effective scaling.

Training vs. inference: which chip for which job?

Understanding training vs. inference helps you pick the right hardware.

  • Training is like teaching a class: you need to process enormous amounts of data, compute gradients, and update billions of parameters. You want maximum parallel math and memory bandwidth. GPUs and TPUs dominate here.
  • Inference is like giving a quiz: given a trained model, produce an answer fast and cheaply. You might prioritize low latency, cost per request, or throughput.

Typical patterns:

  • Large model training: multi-GPU or TPU clusters with high-speed interconnects (NVLink, InfiniBand, TPU interconnects).
  • High-throughput batch inference: GPUs or TPUs with large batches to amortize cost.
  • Low-latency, small models: CPUs can be great, especially with quantized models and libraries like ONNX Runtime or OpenVINO.
  • Edge/mobile: dedicated NPUs or integrated accelerators (e.g., Apple Neural Engine, Qualcomm Hexagon) plus CPUs/GPUs depending on the device.

Where do popular tools fit?

  • ChatGPT: trained on large GPU fleets; inference often runs on optimized GPU infrastructure, with CPUs handling routing, retrieval, and business logic.
  • Claude: similar pattern, with heavy GPU usage for training and serving high-volume inference.
  • Gemini: leverages TPUs for training and serving within Google Cloud at massive scale.

Cost, power, and deployment considerations

Hardware choice is not just about speed. It is also about budget, power, and operational fit.

Key dimensions:

  • Latency vs. throughput: CPUs excel at single-request latency; GPUs/TPUs shine with batches.
  • Cost per token/image: GPUs/TPUs usually win for deep models at scale; CPUs may win for small models or spiky workloads.
  • Power and cooling: High-end GPUs and TPUs are power-dense; ensure your data center or cloud quotas can handle it.
  • Memory capacity: Model size plus batch size must fit accelerator memory. Techniques like quantization, pruning, and LoRA adapters can reduce footprint.
  • Interconnects: For multi-accelerator training, high-speed links (NVLink, TPU interconnect) are crucial to avoid bottlenecks.
  • Ecosystem and skills: CUDA/PyTorch talent is abundant; TPU stacks are excellent but more specialized.

Concrete cost example:

  • If you serve a small LLM (3-7B parameters) with low traffic, a CPU-only server with quantization (e.g., int8/int4) might give you best cost and simplicity.
  • If you serve a large vision model to thousands of users, a single GPU with batched requests can cut cost per request dramatically compared to many CPU instances.

How to choose for your project

Use this checklist to narrow your choice:

  1. What is my model size and modality?
  • Small (<7B parameters) text models or classic ML? CPUs may be fine.
  • Medium to large LLMs or vision transformers? GPUs or TPUs.
  1. What matters more: latency or throughput?
  • Sub-100ms interactive UX? Consider CPUs or small, dedicated GPUs with small batches.
  • Bulk processing (analytics, nightly jobs)? GPUs/TPUs with large batches.
  1. Where will it run?
  • On-prem with existing servers? CPUs/GPUs.
  • Google Cloud with TensorFlow/JAX? TPUs are attractive.
  • Edge/mobile? Use device accelerators plus CPU for control.
  1. What is my team comfortable with?
  • PyTorch/CUDA experience? GPUs are the fastest path.
  • TensorFlow/JAX and Google Cloud? TPUs may deliver better price/performance.

Quick reference by workload

  • Classic ML (XGBoost, scikit-learn): CPU first; GPU can help for large datasets.
  • Image generation (diffusion): GPU recommended.
  • LLM training (billions of params): Multi-GPU or TPU clusters.
  • LLM inference (chat, RAG): GPU for heavy lifting; CPU for retrieval, routing, and postprocessing.
  • Batch analytics with embeddings: GPU for vector ops; CPU for ETL and orchestration.

A note on NPUs and the edge

While this post focuses on CPUs, GPUs, and TPUs, you will also hear about NPUs (Neural Processing Units). These are accelerators built into phones and laptops to speed up on-device AI. Examples include Apple’s Neural Engine and Qualcomm’s Hexagon DSP. They sit alongside the CPU/GPU and can make features like on-device transcription or image effects run fast without cloud calls. For small to medium models and privacy-sensitive use cases, NPUs plus CPUs are compelling.

Putting it together: real-world stacks

  • Startup deploying a support chatbot:

    • CPU nodes for RAG (vector search, re-ranking, formatting).
    • One or two GPUs for batched inference on a 13B model.
    • Result: lower cost per message and faster responses under load.

  • Enterprise vision quality control:

    • GPUs on-prem for real-time inference at the factory line.
    • CPUs coordinate cameras, PLCs, and logging.
    • Result: consistent throughput with deterministic latency.

  • Research team training a new model:

    • Google Cloud TPU v5p for cost-effective scaling with JAX.
    • CPUs handle data pipelines and metrics.
    • Result: faster time-to-train with predictable costs.

Conclusion: make the chip fit the job

You do not need the fastest chip; you need the right chip. Use CPUs for flexibility and orchestration, GPUs for heavy parallel math, and TPUs when you are in the Google ecosystem and want scale and efficiency. Match the hardware to your model size, latency needs, and operational context.

Next steps:

  • Benchmark your current workload on CPU and GPU using a small test set; measure latency, throughput, and cost per request.
  • If you are in Google Cloud with TensorFlow/JAX, run a short trial on TPUs to compare price/performance.
  • Apply model optimization (quantization or distillation) before buying more hardware; often the cheapest acceleration is a smaller model.

With these basics, you can make clear, practical choices that keep your AI fast, affordable, and reliable.