Published 7-23-2026
LLM Advancements Affecting Scalability and Performance
Many people look at systems such as Google's AI Overviews and assume that current AI capabilities represent the limits of what large language models can achieve. In reality, both hardware and software are advancing rapidly, enabling models to run faster, scale to larger parameter counts, and operate on hardware that would have been impractical only a short time ago.
Recent advances include Mixture of Experts (MoE) architectures, low-bit quantization, and improved memory management. Together, these techniques reduce computational requirements, shrink storage footprints, and improve the scalability and efficiency of large language models.
Running a 744-billion-parameter large language model (LLM) locally on a laptop using only a CPU and 25 gigabytes of RAM, with no connection to a data center, may seem impossible. In fact, it has been demonstrated, although the resulting performance is far below what would be considered practical for everyday use.