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Llama 4: Breaking New Ground in Multimodal AI
Artificial intelligence models are growing not just bigger, but also more flexible and accessible. Meta’s newly announced Llama 4 is a prime example: it’s the first natively multimodal open-weight model in the Llama family, built to handle both text and images at scale. Yet as exciting as it sounds, running Llama 4 locally might not be straightforward right out of the gate. Let’s dive in.
1. What’s New in Llama 4 — and Why It’s Different
Multimodality
Most language models today only handle text. Llama 4, on the other hand, was trained from the ground up to process both text and images. Instead of awkwardly bolting on a separate image encoder, it uses a unified “early fusion” approach during pre-training.
- Why it matters: With a single model, you can have it read documents, view pictures, and respond intelligently about both. Potential use cases: describing, comparing, or summarizing multiple images alongside text.
Incredible Context Windows
- Scout, the smaller Llama 4 variant, supports up to 10 million tokens.
- Maverick, the larger variant, supports 1 million tokens.
Remember that 1 token is roughly ~¾ of a word in English; a million tokens is enormous, let alone ten million.
- Why it matters: The model can deal with massive data (entire codebases, large sets of documents, long user histories) all at…
