Meta explains the difference between GPUs, CPUs and its own MTIA chips in its AI infrastructure

Meta has launched an educational series called "Infrastructure Explained," in which the company publicly breaks down what compute power is and how GPUs, CPUs and its own MTIA chips run its AI systems at the scale of several billion users.

Why Meta is explaining the fundamentals

The initiative fits into a broader transparency strategy. Meta has been steadily publishing material on the inner workings of its AI systems, from how its data centers are built to how its recommendation algorithms work. "Compute power" shows up more and more often in CEO statements and quarterly investor reports, but it is rarely explained in plain language for a general audience.

At its most basic, compute power is the total speed at which a system performs mathematical operations. The higher it is, the more complex the models you can train and the faster they run in production. For an AI company, this is literally the foundation: without enough compute, there are no new models, no instant responses for users and no real-time recommendation feeds.

The context matters here. The world's largest tech companies are now competing hard for exactly this resource: compute capacity. Meta plans to spend $60–65 billion on AI infrastructure in 2025 alone, so explaining why it needs that kind of money has become a necessity.

Three types of processors in Meta's arsenal

Meta builds its AI infrastructure on a combination of three architectures, each with its own role:

• GPUs (graphics processing units) are the main workhorse for training neural networks. Thousands of parallel cores handle the matrix math at the heart of most AI algorithms. Every version of the open Llama model was trained on GPUs.
• CPUs (central processing units) handle system logic, coordinate tasks and process data that doesn't require massive parallelism.
• MTIA (Meta Training and Inference Accelerator) is Meta's own custom chip, built specifically for its workloads: recommendation systems, content ranking on Facebook and Instagram, and inference for Llama models.

Combining the three architectures lets Meta distribute workloads efficiently: it avoids burning expensive GPU time where a CPU will do, and it brings in MTIA where it needs specialized speed with lower power consumption.

Why Meta is building its own chips

Relying on outside suppliers, above all NVIDIA, creates supply chain risks and limits how finely the company can optimize for its specific tasks. The GPU market has been in severe shortage for the past two years, with wait times for the new Hopper and Blackwell series stretching months ahead.

General-purpose chips also have to support a wide range of tasks, which inevitably means performance trade-offs. Meta designed MTIA around its own models and compute patterns, which gives it an edge in efficiency and cost per operation.

An in-house chip lets Meta:

• cut inference costs with an architecture tailored to specific models;
• iterate faster on development without depending on third-party vendors' roadmaps;
• scale capacity without competing for scarce chips from other vendors;
• optimize power consumption for the specifics of its own workloads.

"We believe this is one of the most important investments we're making right now,"

Mark Zuckerberg said when he announced plans to put $60–65 billion into AI infrastructure in 2025.

What it means

Meta's educational materials aren't simply corporate PR. A company that can explain complex topics accessibly wins on several fronts at once: it attracts engineering talent, shapes the narrative for regulators and builds trust with partner developers.

For everyday users, this kind of material is a reminder that every AI assistant response and every feed recommendation is backed by tons of physical hardware and billions of dollars in investment.

For the industry as a whole, it sends a broader signal: custom silicon is no longer the privilege of Apple or Google. Every major AI company now builds its own hardware, and the power of that hardware will determine who sets the pace for the next generation of AI systems.

*Meta is designated an extremist organization and banned in Russia.