The recent launch of Llama-3.1-Storm-8B marks a significant advancement in large language models (LLMs), outperforming competitors such as Meta's LLaMA and Hermes across various benchmarks. This new model, developed by Homebrew Research, features Llama3-s v0.2, which introduces enhanced multimodal capabilities, allowing it to understand both audio and text inputs. The model utilizes an innovative early fusion approach with semantic tokens, processing audio through a WhisperVQ encoder before generating text responses. The Llama3 tokenizer has also been integrated into GPT-2 training, expanding its vocabulary size to 128,001 tokens compared to GPT-2's 50,257 tokens. The advancements in Llama-3.1's capabilities are indicative of the growing trend towards multimodal AI systems, which are expected to offer broader applications in natural language processing and real-time interaction. Companies are also launching new features to support multimodal monitoring across various AI models, enhancing the infrastructure for LLMs and audio models.