Recent advancements in AI model efficiency have led to significant improvements, with the introduction of Dynamic Memory Compression (DMC) and the development of EagleX 7B. DMC allows Large Language Models (LLMs) to compress their Key-Value (KV) cache, maintaining performance while addressing the issue of memory growth linear with sequence length at inference time. This approach is seen as a step towards optimizing transformer inference speed and reducing redundancy in memory usage. EagleX 7B, utilizing only 1.7 trillion tokens and licensed under Apache2 OpenWeights, has outperformed the LLaMA 7B model in English and multi-lingual evaluations, despite LLaMA 7B's use of 2 trillion tokens. This achievement is notable for being attention-free and surpassing the gold standard for 7B transformer models. The improvements in multi-lingual performance and inference cost, which is reported to be 10-100 times lower, along with a ~4x throughput increase at inference time, mark a significant shift in AI model development, potentially changing the landscape of what and where AI can be built and applied.