Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing
As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have outsized financial impact in fast-moving AI hardware cycles. Simultaneously, the shift from chip-level optimization to system-level engineering is compounding thermal and power challenges. Meeting these demands requires breakthroughs…
What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....
The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...

Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage...

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances—including high bandwidth memory (HBM), NVIDIA...
AI companions in games have long been constrained by fixed dialogue. PUBG Ally is a different kind of system. Built by KRAFTON for PUBG: BATTLEGROUNDS, this AI...
Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating...
NVIDIA RTX technologies are deeply integrated into Unreal Engine 5 through the NVIDIA RTX Branch of Unreal Engine and the NVIDIA DLSS Unreal Engine plugin. This...
AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale....
AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale...