Everyone is not just talking about AI inference processing; they are doing it. Analyst firm Gartner released a new report this week forecasting that global generative AI spending will hit $644 billion ...

Forbes contributors publish independent expert analyses and insights. Victor Dey is an analyst and writer covering AI and emerging tech. As OpenAI, Google, and other tech giants chase ever-larger ...

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 ...

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 ...

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 ...

Nvidia is aiming to dramatically accelerate and optimize the deployment of generative AI large language models (LLMs) with a new approach to delivering models for rapid inference. At Nvidia GTC today, ...

The major cloud builders and their hyperscaler brethren – in many cases, one company acts like both a cloud and a hyperscaler – have made their technology choices when it comes to deploying AI ...

GPUs’ ability to perform many computations in parallel make them well-suited to running today’s most capable AI. But GPUs are becoming tougher to procure, as companies of all sizes increase their ...

The CNCF is bullish about cloud-native computing working hand in glove with AI. AI inference is the technology that will make hundreds of billions for cloud-native companies. New kinds of AI-first ...

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 ...

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 ...