Millions of Americans type questions into Facebook, Instagram, and WhatsApp every day and get an instant answer from Meta AI. Most users assume a single, enormous brain is reading every word and firing every neuron at once.
The reality is far stranger, and far more clever.
The AI underneath those familiar chat boxes runs on a model called Llama 4 Maverick. And the way it actually processes your words is nothing like the image most people carry around.
Why the size of an AI model is not the whole story
For years, the AI benchmark that mattered most to the public was raw parameter count.
Parameters are roughly the adjustable weights a model learns during training. More parameters, the thinking went, meant a smarter model. A bigger number on the box.
That framing made sense for older, so-called dense architectures, where every single parameter fires for every single token the model reads.
The bigger you made it, the more every inference cost in electricity and time.
But a new generation of models changed the equation entirely, and most users have no idea the switch happened.
What happens inside the model when you hit send
When you type a message to Meta AI, your words arrive as individual tokens, the basic units the model actually reads and routes.
In an older dense model, every token would activate the entire parameter set, all at once, on every layer of the network.
Llama 4 Maverick does not do that.
Instead, its architecture is sliced into layers that contain banks of parallel sub-networks, each one a specialist tuned to a different kind of reasoning.
A learned routing function reads the token and decides in real time which specialist gets the job.
The staggering gap between the full model and the active slice
Llama 4 Maverick holds 400 billion total parameters, yet a single token activates only a fraction of them at a time.
That is not a bug. It is the entire design.
A 400-billion-parameter dense model would require 400 billion parameters’ worth of compute per inference call. The mixture-of-experts approach changes this: the model contains many specialized expert sub-networks, and a learned routing function selects only a small subset to activate per token.
Llama 4 Maverick has 400 billion total parameters but only 17 billion active at any given time.
That means inference costs track closer to a 17-billion-parameter model while the stored knowledge of something far larger stays on tap.
The routing mechanism that makes it possible
This is the real reveal: the system running inside the AI you already use is called mixture of experts, and Meta’s official blog describes how Llama 4 Maverick implements it in surprisingly specific detail.
The model uses alternating dense and mixture-of-experts layers for inference efficiency. Each MoE layer uses 128 routed experts and a shared expert. Each token is sent to the shared expert and also to one of the 128 routed experts.
As a result, while all parameters are stored in memory, only a subset of the total parameters are activated while serving these models.
This improves inference efficiency by lowering model serving costs and latency, and Llama 4 Maverick can be run on a single NVIDIA H100 DGX host for easy deployment, specifically using its FP8 quantized weights, as the FP8 quantized weights fit on a single H100 DGX host while still maintaining quality.
Compare that to the previous generation: a standard dense model like GPT-3 activates all 175 billion parameters for every single token it processes.
The routing decision for each token happens in microseconds, invisibly, every time you type a sentence. For context on how AI is evolving more broadly, the GPT-5.6 launch introduced its own three-tier model structure, while Google’s Gemini API updates show rival labs racing to match Llama 4’s efficiency gains.
Why this matters for every American paying for AI
MoE architectures are more compute efficient for training and inference and, given a fixed training compute budget, deliver higher quality compared to a dense model.
That efficiency gap flows directly into price. With only 17 billion parameters active per token, Maverick’s FP8 weights are deployable on a single high-end server node for inference, making it accessible to a much wider range of organizations than its 400-billion total parameter count might suggest.
Meta also released the model as open-weight, meaning any qualifying developer can download it and run it.
Released on April 5, 2025, Llama 4 marks Meta’s first model family to use a mixture-of-experts architecture, delivering dramatic efficiency gains while pushing the boundaries of what open-weight models can achieve.
The routing trick that fires only 17 billion parameters out of 400 billion is, in a real sense, why AI assistants can afford to answer your questions at all.
The next time an answer pops up instantly in your Instagram DMs, that invisible switchboard just did its job in the time it took you to blink.
