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 [...]
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 [...]
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 [...]
While Elon Musk faces off against his former colleague and OpenAI co-founder Sam Altman in court, Musk's rival firm xAI, founded to take on OpenAI, isn't slowing down on launching competitiv [...]
DeepSeek continues to push the frontier of generative AI...in this case, in terms of affordability.The company has unveiled its latest experimental large language model (LLM), DeepSeek-V3.2-Exp, that [...]
The AI updates aren't slowing down. Literally two days after OpenAI launched a new underlying AI model for ChatGPT called GPT-5.3 Instant, the company has unveiled another, even more massive upgr [...]
It's been almost one year since Intuit shut down the popular budgeting app Mint. I was a Mint user for many years; millions of other users like me enjoyed how easily Mint allowed us to track all [...]
Processing 200,000 tokens through a large language model is expensive and slow: the longer the context, the faster the costs spiral. Researchers at Tsinghua University and Z.ai have built a technique [...]
Researchers at Mila have proposed a new technique that makes large language models (LLMs) vastly more efficient when performing complex reasoning. Called Markovian Thinking, the approach allows LLMs t [...]