This brute-force scaling approach is slowly fading and giving way to innovations in inference engines rooted in core computer ...

Snowflake has thousands of enterprise customers who use the company's data and AI technologies. Though many issues with generative AI are solved, there is still lots of room for improvement. Two such ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...

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 ...

The generative AI market is experiencing rapid growth, driven by the increasing parameter size of Large Language Models (LLMs). This growth is pushing the boundaries of performance requirements for ...

Sponsored Feature: Training an AI model takes an enormous amount of compute capacity coupled with high bandwidth memory. Because the model training can be parallelized, with data chopped up into ...

A decade ago, when traditional machine learning techniques were first being commercialized, training was incredibly hard and expensive, but because models were relatively small, inference – running ...

“I get asked all the time what I think about training versus inference – I'm telling you all to stop talking about training versus inference.” So declared OpenAI VP Peter Hoeschele at Oracle’s AI ...

Despite ongoing speculation around an investment bubble that may be set to burst, artificial intelligence (AI) technology is here to stay. And while an over-inflated market may exist at the level of ...