Presented by NutanixAs enterprises move from AI experimentation into production deployment, the primary cost driver has shifted away from foundation model training and toward the infrastructure required to run thousands of concurrent inference workloads at scale, with agentic AI as the accelerant. Where early enterprise AI projects involved a handful of large, scheduled training jobs, production agentic environments require continuous support for short-lived, unpredictable requests that consume GPU, networking, and storage resources in ways traditional infrastructure was never designed to handle. For enterprise technology leaders, that shift is turning infrastructure efficiency into a make-or-break factor in AI economics. "Every employee with an AI assistant, every automated workflow, [...]
DeepSeek, the Chinese artificial intelligence research company that has repeatedly challenged assumptions about AI development costs, has released a new model that fundamentally reimagines how large l [...]
To quote an ancient Jedi Master "Begun, the AI price wars have!"OpenAI is sharply reducing the prices of two models in its GPT-5.6 frontier series, cutting GPT-5.6 Luna, the smallest and fas [...]
Even as the geopolitical conversation around AI continues to grow more fraught following the U.S. government's actions to limit the new models from Anthropic and OpenAI, Chinese open source darli [...]
DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]
Elon Musk's company SpaceXAI, formerly known as xAI, has released Grok 4.6, its latest frontier AI model, with a focus on long-running agents, coding and knowledge work — and a pricing strategy [...]
As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a [...]
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]
Google unveiled Gemini 3.5 Flash at its annual I/O developer conference on Tuesday, a new artificial intelligence model that the company says shatters what had become a seemingly iron law of the AI in [...]