Why Meta Is Turning to Google TPUs And Why NVIDIA Is Still Market Leader

A story about two very different machines shaping the future of AI.

In late 2024, the AI hardware world witnessed a quiet but significant shift: Meta began training large portions of its Llama 3 and 4 models using Google’s TPU (Tensor Processing Unit) pods.

For a company that famously buys hundreds of thousands of NVIDIA GPUs, this move was surprising. Why would Meta—an empire built on GPUs—suddenly embrace Google’s custom silicon?

To understand this, you have to look at the fundamental difference between these two technologies:

• TPUs are like freight trains. They are unmatched at hauling massive, heavy loads in a straight line across long distances.

• GPUs are like a fleet of agile 18-wheelers. They can handle almost any cargo, drive on any road, and take shortcuts when needed.

Meta realizes it needs both. As one engineer reportedly explained:

"If you want to train a massive model across thousands of chips with perfect predictability, TPUs act like one giant super-machine. But if you want to invent new architectures or run fast apps for users, NVIDIA still rules."

This isn't corporate politics. It is pure engineering.

Part 1: The Problem with Training Giants on GPUs

When Meta started training the early versions of Llama 3, they hit a few roadblocks common to GPU clusters.

The "Traffic Jam" Problem

GPU clusters are often messy. Not every rack of servers is connected the same way. Some cables communicate at blazing speeds (900 GB/s), while others are slower or have to hop through multiple switches.

• The Result: When you try to sync data across thousands of chips, the whole system can only move as fast as the slowest connection. It becomes unpredictable.

The "Generalist" Tax

NVIDIA GPUs are incredible because they can do anything—graphics, physics simulations, and AI. But that flexibility comes with a cost. They aren't 100% optimized just for the specific math (dense linear algebra) that Transformers need. When you are running a trillion-dollar calculation, those tiny inefficiencies add up to millions of dollars in wasted energy.

Part 2: Enter the Google TPU

Google’s TPUs are different. They aren't designed to do "everything." They are designed to do exactly one thing: Matrix Math.

A TPU pod is built to act like a single, giant supercomputer.

Why Meta Likes TPUs for Training:

1. They Scale Perfectly: TPUs use a "3D Torus" connection. Imagine a cube where every chip has a direct, evenly spaced connection to its neighbors. There are no traffic jams.

2. Factory Line Efficiency: TPUs use "systolic arrays." Instead of moving data back and forth constantly (which wastes energy), data flows through the chip like a factory assembly line, performing calculations rhythmically.

3. Strategic Safety: Relying 100% on NVIDIA is risky. By using Google’s chips, Meta creates competition and ensures they have a backup plan.

For the brutal, months-long task of training a base model, TPUs offer a predictable highway.

Part 3: Why NVIDIA Still Owns the Kingdom

It might sound like Google is winning, but NVIDIA holds the crown for very good reasons. As a Meta engineer noted, "Training is bulk labor. Inference (running the app) is a craft."

Here is why NVIDIA is not going anywhere:

1. Ultimate Control (The "Craft" of Inference)

When you chat with an AI, the computer has to do complex, irregular tricks to answer you quickly. It needs to manage memory dynamically and skip unnecessary calculations.

• TPUs struggle with this irregularity. They like straight lines.

• GPUs are programmable down to the metal. Using NVIDIA's CUDA software, engineers can tweak every single aspect of how the chip works. This is essential for making AI fast enough for real-time chat.

2. The Ecosystem Moat

NVIDIA has spent 20 years building software tools (CUDA, TensorRT) that every developer knows how to use. A TPU engineer once joked, "TPUs are fast, but NVIDIA owns the universe."

3. Versatility

Most AI isn't just text. It’s images, video, and physics. GPUs excel at:

• Diffusion models (image generation)

• Robotics simulations

• Sparse operations (doing math with lots of zeros)

4. The FP8 Advantage

Newer NVIDIA chips (H100/Blackwell) support FP8 (8-bit floating point precision). This allows them to process data incredibly fast with lower precision, which is often "good enough" for many tasks. For many mid-sized models, these GPUs are actually faster than TPUs.

TPUs win on training efficiency (bulk processing), while GPUs win on serving flexibility.

Llama 3 (70B) Cost-Per-Token Estimates

The table below breaks down the estimated "Cost-Per-Million-Tokens" for both Training (processing tokens to learn) and Inference (generating tokens for users).

Workload	Metric	Google TPU v5p Pod	NVIDIA H100 Cluster	The Winner
TRAINING (Meta's Use Case)	Cost per 1M Training Tokens	~$0.90 – $1.10	~$1.50 – $1.80	TPU v5p (~30-40% Cheaper)
	Throughput	Extreme (Linear Scaling) Scales to 10k+ chips easily	High Diminishes slightly >4k chips	TPU (at massive scale)
	Hardware Cost	~$2.94 / chip-hour (3-yr commit)	~$3.50 – $4.50 / GPU-hour (Standard Cloud)	TPU
INFERENCE (Serving Users)	Cost per 1M Output Tokens	~$1.80 – $2.50 (v5p is overkill for this)	~$0.60 – $0.90 (Highly Optimized)	NVIDIA H100 (3x Cheaper)
	Latency (Speed)	Good (Batch optimized)	Excellent (Interactive optimized)	NVIDIA
	Flexibility	Rigid (Requires batching)	High (vLLM, continuous batching)	NVIDIA

Conclusion: The Future is Hybrid

• TPUs behave like a massive conveyor-belt factory - Stream data once → compute → minimal memory bouncing

• GPUs behave like a programmable workshop - Multiple memory stages → flexible kernels → optimize anything

• TPUs dominate dense algebra training

• GPUs dominate inference, custom ops, multi-modal, vision, MoE

• Meta needs both:

  • TPUs for pretraining huge LLMs

  • GPUs for fine-grained control & global deployment

  
  

We are moving toward a world where companies will use the right tool for the right job.

World 1: Training at Ridiculous Scale

• Goal: Train a 400 Billion parameter model over 3 months.

• Winner: TPUs. You want the efficiency of the "freight train" and the uniform connections.

World 2: Serving the Model to Users

• Goal: Answer a user's question in 0.5 seconds cheaply.

• Winner: GPUs. You need the flexibility, the custom software tricks, and the ecosystem availability.

Meta knows this. Google knows this. And NVIDIA definitely knows this.

The rise of the TPU doesn't mean the fall of the GPU. It just means the AI industry is finally building the specialized infrastructure it needs to grow.