Meta Unveils Llama 4 Multimodal AI

Llama 4 builds on the foundation of Llama 3, which was pretrained on over 15 trillion tokens from public sources, a dataset seven times larger than that of Llama 2. Llama 3's architecture introduced a tokenizer with a 128,000-token vocabulary for more efficient language processing and used Grouped Query Attention (GQA) to improve inference efficiency. The move into native multimodality places Llama 4 in direct competition with models like OpenAI's GPT-4o and Google's Gemini, which were designed from the ground up to process and reason across text, images, and audio. This competitive landscape is defined by ever-expanding context windows, with models like Gemini 1.5 supporting up to 1 million tokens, and a constant push for lower inference latency. For an ML engineering portfolio, Llama 4's capabilities suggest projects beyond simple fine-tuning. A standout project could be building a full-stack Retrieval-Augmented Generation (RAG) system that ingests and indexes both text and images into a vector database, serving a cross-modal search API. This demonstrates skills in data pipelines, vector search, and model serving. The introduction of native multimodality presents a new class of ML system design interview questions. Candidates may be asked to architect a scalable inference service that handles heterogeneous inputs (e.g., text and images) with different SLOs for latency and throughput, requiring sophisticated request batching and GPU resource management. Meta's strategy with the Llama series has been to treat open-source AI as a competitive advantage to build a wide ecosystem, much like the Linux of AI. By releasing model weights, Meta encourages broad adoption and innovation, aiming to make its architecture an industry standard that prevents competitors from locking down the market. However, there are reports of internal debate at Meta regarding a potential