LLaMA 2

LLaMA 2, developed by Meta AI, is a generation of large language models ranging from 7 billion to 70 billion parameters. Built upon the transformer architecture, it introduced significant improvements over its predecessor, including Grouped-Query Attention (GQA) for the 70B model to enhance inference efficiency and a doubling of the context length to 4,096 tokens. In the 2026 landscape, LLaMA 2 remains a critical benchmark for enterprise-grade, self-hosted deployments where data privacy is paramount. It was pre-trained on 2 trillion tokens and fine-tuned using Reinforcement Learning from Human Feedback (RLHF), specifically optimized for dialogue through its 'Llama-2-chat' variants. While newer iterations like Llama 3 and 4 have since been released, LLaMA 2 maintains a significant market share due to its stability, extensive documentation, and lighter computational footprint for edge-computing applications. It serves as the primary backbone for businesses requiring local LLM orchestration without the latency or privacy risks associated with proprietary cloud APIs. Its licensing model remains a standard-bearer for 'open-ish' AI, permitting commercial use for entities with fewer than 700 million monthly active users, making it the preferred choice for startups and mid-market enterprises looking to escape vendor lock-in.