Llama 4 Series Overview
In one of the most significant AI unveilings of 2025, Meta has officially launched its Llama 4 series. A trio of large language models is designed to tackle everything from real-time mobile inference to artificial general intelligence (AGI) research. The models, dubbed Llama 4 Scout, Maverick, and Behemoth, aren’t just incremental upgrades. They represent a strategic rethinking of how AI should scale across devices, industries, and scientific frontiers.
Scout: Speed and Efficiency in Your Pocket
First in the lineup is Llama 4 Scout, Meta’s leanest and fastest model, engineered specifically for on-device usage. With an estimated size of under 20 billion parameters, Scout prioritizes low latency and high efficiency. This makes it ideal for smartphones, AR glasses like Meta Ray-Bans, and other edge devices.
What sets Scout apart is its highly optimized architecture. Meta reportedly used advanced attention mechanisms such as linear or sparse attention, combined with aggressive quantization and pruning techniques. The result? Blazing-fast performance without sacrificing too much intelligence.
Despite its smaller size, Scout has undergone fine-tuning via reinforcement learning with human feedback (RLHF). This ensures it can maintain coherence, factual accuracy, and safe responses in real-world scenarios.
Maverick: The All-Purpose Powerhouse
Next comes Llama 4 Maverick, the versatile middle sibling that’s designed to take on GPT-4 Turbo head-to-head. With an estimated parameter count between 30B and 65B, Maverick balances scalability, cost-efficiency, and raw capability. It’s Meta’s go-to model for enterprise applications, productivity tools, intelligent assistants, and everything in between.
Built on a dense decoder-only transformer architecture, Maverick is speculated to use multi-query attention (MQA). It supports extended context lengths — potentially up to 128,000 tokens. This makes it ideal for long-form document analysis, code generation, and conversational AI tasks that require memory and depth.
Training-wise, Maverick leverages trillions of tokens across web data, books, multilingual corpora, and proprietary datasets. It also benefits from advanced alignment techniques, including auto-critique systems and synthetic feedback loops.
Behemoth: Meta’s Bid for the AI Throne
Last but certainly not least is Llama 4 Behemoth — a massive model with over 100 billion parameters. It is built to push the very limits of AI research and development. Designed to go toe-to-toe with the largest models from OpenAI and Anthropic, Behemoth is Meta’s flagship for AGI-level tasks.
Meta blog post suggests that Behemoth could reach up to 200 billion parameters. The focus is on complex reasoning, symbolic logic, advanced code generation, and possibly even multi-modal capabilities. Its training reportedly occurred on Meta’s Research SuperCluster (RSC). It’s a state-of-the-art AI supercomputer equipped with thousands of GPUs and optimized interconnects.
This model isn’t just about scale. It’s about setting a new bar for what AI can do in academic benchmarks, human evaluation, and cross-domain generalization.
Three Models. One Vision.
By releasing three distinct models under the Llama 4 umbrella, Meta is signaling a bold vision: AI isn’t one-size-fits-all. While Scout brings intelligent computing to everyday devices, Maverick anchors real-world enterprise needs. And Behemoth ventures into the experimental frontier of artificial general intelligence.
Together, they form a cohesive AI ecosystem that scales intelligently across use cases. This is a strategic move that could redefine Meta’s role in the ongoing AI race.
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