Llama 4 Explained: Architecture, Long Context, and Native Multimodality

Curious how Meta’s Llama 4 works under the hood? In this deep dive, I reverse-engineer the Llama 4 architecture based on Meta’s official blog post and unpack the innovations that enable its 10M token context window and native multimodality. ✅ What makes Llama 4 natively multimodal? ✅ How does it support long context lengths? Is RAG obsolete? ✅ How good is it *really*? 🔍 Topics covered (with papers): 🔵 Early fusion (https://arxiv.org/pdf/2405.09818) 🔵 Context Parallelism / Ring Attention (https://arxiv.org/pdf/2310.01889) 🔵 Rotary Positional Embeddings / RoPE (https://arxiv.org/pdf/2104.09864) 🔵 Position Interpolation (https://arxiv.org/pdf/2306.15595) 🔵 No Positional Embeddings / NoPE (https://arxiv.org/pdf/2305.19466) 🔵 New training strategies: Mid-training, MetaP This video is ideal for engineers and researchers curious about how LLMs scale, why Llama 4 matters, and what's next for long-context transformers. 📌 Note: This is a corrected re-upload due to A/V sync issues in the previous version. #Llama4 #MetaAI #MultimodalLLM #LongContext 00:00 Intro 00:55 Behemoth, Maverick, Scout & Mixture-of-Experts 02:36 Multimodality in Llama 3 05:02 Native multimodality in Llama 4 08:27 10M context window 09:41 Ring Attention 12:28 Length generalization 16:56 New training techniques 20:21 Is RAG dead? 21:08 Evaluation