Llama-4

For years, the dominant approach to multimodal AI followed a simple recipe: take a pre-trained vision encoder (CLIP, SigLIP), bolt it onto a pre-trained LLM through an adapter layer, and fine-tune the connection. This “late-fusion” paradigm powered everything from GPT-4V to LLaVA, delivering impressive results with remarkable sample efficiency. But a fundamental question lingered: was this architectural shortcut an inherent advantage, or merely a convenient workaround? The answer arrived in 2025 with a paradigm shift that’s rewriting the rules of multimodal AI. Native multimodal models—trained from scratch on all modalities simultaneously—are proving that early-fusion architectures don’t just match late-fusion approaches; they exceed them in efficiency, scalability, and ultimately, capability. ...

In April 2025, Meta’s Llama 4 Scout achieved something previously thought impossible: processing 10 million tokens in a single context window. To put this in perspective, that’s roughly 20 novels, 40 hours of video, or an entire mid-sized codebase—all in one prompt. The secret behind this breakthrough isn’t a revolutionary new model architecture or exotic hardware. It’s a clever distributed computing technique called Ring Attention that fundamentally rethinks how we compute attention across multiple GPUs. ...