A recent industry letter, “Open Weights and American AI Leadership,” argues that open-weight models, trained AI models that are available to download, are essential to a competitive and broadly accessible AI ecosystem.  The letter makes a compelling case....

Fine-tuning larger AI models often becomes constrained by GPU memory before compute capacity is exhausted. This article explains why weights, optimizer states, gradients, and activations drive memory demand, why adding GPUs can be an expensive workaround, and how...

Learn how KV cache growth silently degrades AI inference performance and why memory-aware infrastructure is becoming essential for scaling modern AI workloads.  AI inference performance often slows long before GPUs reach their compute limits because growing KV...

Mixture of Experts architecture delivers larger-model capability while activating only a fraction of the model’s parameters per token. That makes it a strong fit for local AI, but the full model still has to remain accessible in memory. Here’s why that matters and...