Training gets the hype, but inferencing is where AI actually works — and the choices you make there can make or break ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

Many theories and tools abound to aid leaders in decision-making. This is because we often find ourselves caught between two perceived poles: following gut instincts or adopting a data-driven approach ...

Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...

The race to build bigger AI models is giving way to a more urgent contest over where and how those models actually run. Nvidia's multibillion dollar move on Groq has crystallized a shift that has been ...

Nvidia has long dominated the market in compute hardware for AI with its graphics processing units (GPUs). However, the Spring 2024 launch of Cerebras Systems’ mature third-generation chip, based on ...

Historically, we have used the Turing test as the measurement to determine if a system has reached artificial general intelligence. Created by Alan Turing in 1950 and originally called the “Imitation ...

In recent years, the big money has flowed toward LLMs and training; but this year, the emphasis is shifting toward AI ...

AMD is strategically positioned to dominate the rapidly growing AI inference market, which could be 10x larger than training by 2030. The MI300X's memory advantage and ROCm's ecosystem progress make ...