Until recently, OpenAI reigned unchallenged in the field of large language models (LLMs). However, in 2024, companies like Meta, Google DeepMind, and Anthropic managed to narrow the gap significantly. For instance, Meta’s new Llama 3 series has, for the first time, reached a performance level comparable to closed-source models such as GPT-4.
Models now do more than just generate text—they can learn programming, solve mathematical problems, analyze images, and even process biological data. Leading enterprises are betting on multimodal models that can handle multiple types of information simultaneously: text, images, video, and even molecules.
A central focus is optimization and cost reduction in AI. Organizations are implementing quantization (reducing model size), distillation (transferring knowledge from larger models to smaller ones), and decreasing parameter counts without sacrificing accuracy. These approaches make it possible to run powerful models directly on smartphones.