Compute Infrastructure for Generative AI: GPUs vs TPUs and Distributed Training Explained

Training a model like GPT-4 or Gemini isn't just about writing code. It's about compute. And not just any compute. You need hardware built from the ground up to handle trillions of mathematical operations in seconds. That's where GPUs and TPUs come in - and why distributed training isn't optional anymore. If you're building or using generative AI today, understanding these systems isn't optional. It's the foundation.

What Makes AI Hardware Different?

Regular CPUs? They’re great for running your email client or web browser. But train a large language model? They’d take years. That’s why AI relies on specialized accelerators: GPUs and TPUs. These aren’t just faster versions of your graphics card. They’re different machines designed for one thing: parallel math.

Think of it like this: training a model is like solving a million tiny math problems at once. GPUs, made popular by NVIDIA, have thousands of small cores that work together. TPUs, built by Google, are even more focused - they’re custom chips designed only for tensor operations, the core math behind neural networks. Neither is "better" overall. But one might be far better for your specific job.

GPUs: The Industry Standard

NVIDIA’s H100 and H200 GPUs dominate the market. Why? Because they’re flexible. They run PyTorch, TensorFlow, JAX - almost anything. If you’re experimenting, debugging, or fine-tuning a model on 8 GPUs in a single server, GPUs are your go-to. They’re everywhere: AWS, Azure, GCP. You can rent them by the hour, tweak your code, and restart without rewriting everything.

Here’s what they offer:

• 80GB to 141GB of HBM memory per chip
• ~3,800 tokens per second per H100 chip
• NCCL for communication between GPUs - fast, but not perfect
• Strong support for CUDA, the most used AI programming ecosystem
• Easy to use with PyTorch’s eager mode - great for prototyping

But they have limits. When you scale beyond 100 GPUs, things get messy. Networking between servers becomes a bottleneck. Latency creeps in. Power use climbs. And cost? An 8-chip H100 node runs $12-$15 per hour. That adds up fast when you’re training for weeks.

TPUs: Google’s Secret Weapon

Google didn’t build TPUs to compete with NVIDIA. They built them because their own models - like Gemini - needed something better. The TPU v5p is a beast: 3,672 TFLOPS of compute, 760GB of total memory in an 8-chip slice, and a design that minimizes idle time. While H100s hit about 52% of their theoretical peak performance (called MFU), TPUs often hit 58% - meaning less wasted power, less waiting.

But the real win isn’t speed. It’s cost.

• TPU v5p-8 slice: $8-$11 per hour
• ~3,450 tokens per second per chip
• 70% cheaper on Spot instances
• 2.8x faster training than TPU v4
• 2.1x better value-for-money than TPU v4

And then there’s the TPU v6e - released in late 2025. It delivers up to 4x better price-performance than H100 for LLM training and inference. That’s not a small edge. That’s a game-changer for companies training models with over a trillion parameters.

TPUs don’t use traditional networking. Instead, they connect via an Optical Circuit Switch (OCS) - a dedicated, low-latency, high-bandwidth fabric built into the TPU Pod. A single pod can link 4,096 chips. No congestion. No packet loss. Just linear scaling. That’s why Google trains Gemini on TPUs. It’s not branding - it’s physics.

Distributed Training: How the Magic Happens

You can’t just plug 100 GPUs into a rack and expect them to work together. You need software that coordinates every chip. That’s distributed training.

On GPUs, it’s torch.distributed with NCCL. You write code, tell it how many GPUs to use, and it splits the work. But if one GPU is slow, the whole batch waits. It’s like a group of runners waiting for the slowest person to finish before they all move forward.

On TPUs, it’s GSPMD - Google’s automatic sharding compiler. You write code as if you’re training on one chip. The compiler figures out how to split the model across hundreds of chips. No manual sharding. No complex code. Just run. And it works because TPUs are designed as a single system, not a cluster of separate machines.

This matters most at scale. Training a 100B-parameter model on 512 GPUs? You’ll spend weeks fixing network issues. On a TPU Pod? You might not even notice the difference.

Cost Isn’t Just About Hourly Rates

It’s easy to compare $12/hour for H100 vs $10/hour for TPU. But real cost is more complex.

Consider this: Anthropic, the company behind Claude, reportedly cut their total cost of ownership (TCO) by 52% per PFLOP by switching from NVIDIA GB300 NVL72 to TPUs. Why? Because TPUs are more efficient at using their raw power. Even if they’re running at only 19% of their theoretical peak (a low MFU), they still outperform NVIDIA systems on cost.

That’s the TPU advantage: efficiency. They don’t need to be running at 90% to be cheaper. They’re designed to get the job done with less waste.

And then there’s availability. Google Cloud offers large TPU blocks with far higher uptime than NVIDIA GPU clusters. If you need 256 chips for a week-long training run, you’re more likely to get them on TPU than on GPU.

When Should You Use Which?

There’s no one-size-fits-all. Here’s a simple guide:

• Use GPUs if: You’re doing research, prototyping, or fine-tuning. You need to run custom layers, use PyTorch’s eager mode, or deploy across AWS, Azure, and GCP without rewriting code.
• Use TPUs if: You’re training large foundation models (100B+ parameters), serving millions of users via inference, or you’ve already built on TensorFlow/JAX. You care more about cost-per-token than flexibility.
• Use both if: You’re serious about scaling. Train on TPUs. Serve on GPUs. Or use GPUs for experimentation and TPUs for production. Most top AI labs do exactly this.

For example: You might train a new model on a TPU Pod for 3 weeks. Once it’s stable, you export it and run inference on a cluster of NVIDIA L40s across multiple clouds. That’s not a compromise. That’s strategy.

The Future: Hybrid Is the New Normal

Five years ago, you picked one platform and stuck with it. Today? The smartest teams mix and match. NVIDIA’s ecosystem is still unmatched. But Google’s TPU v6e is changing the math. For companies training models at scale, the cost advantage is too big to ignore.

What’s next? TPUs will get even more efficient. NVIDIA will respond with new architectures focused on memory bandwidth and power. But the real shift isn’t in hardware - it’s in mindset. The best infrastructure isn’t the fastest chip. It’s the one that fits your workflow, your budget, and your team’s skills.

By 2026, if you’re still choosing between GPUs and TPUs as if they’re rivals - you’re missing the point. They’re tools. Use the right one for the job. And if you’re building the next big model? You’ll probably use both.