venturebeat
The compute rethink: Scaling AI where data lives, at the edge

Presented by ArmAI is no longer confined to the cloud or data centers. Increasingly, it’s running directly where data is created — in devices, sensors, and networks at the edge. This shift toward on-device intelligence is being driven by latency, privacy, and cost concerns that companies are confronting as they continue their investments in AI. For leadership teams, the opportunity is clear, says Chris Bergey, SVP and GM, of Arm’s Client Business: Invest in AI-first platforms that complement cloud usage, deliver real-time responsiveness, and protect sensitive data. "With the explosion of connected devices and the rise of IoT, edge AI provides a significant opportunity for organizations to gain a competitive edge through faster, more efficient AI," Bergey explains. "Tho [...]

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venturebeat
Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]

Match Score: 130.54

venturebeat
Moonshot’s Kimi K2.5 is 'open,' 595GB, and built for agent swarms — Reddit wants a smaller one

Two days after releasing what analysts call the most powerful open-source AI model ever created, researchers from China's Moonshot AI logged onto Reddit to face a restless audience. The Beijing-b [...]

Match Score: 72.77

venturebeat
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

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 [...]

Match Score: 71.25

venturebeat
Google’s new framework helps AI agents spend their compute and tool budget more wisely

In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool [...]

Match Score: 65.88

venturebeat
New memory framework builds AI agents that can handle the real world's unpredictability

Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]

Match Score: 64.22

venturebeat
Thinking Machines challenges OpenAI's AI scaling strategy: 'First superintelligence will be a superhuman learner'

While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of [...]

Match Score: 53.31

Destination
Sam Altman says scaling up compute is the "literal key" to OpenAI's revenue growth

OpenAI CEO Sam Altman says scaling up compute will drive both AI breakthroughs and the company's revenue.<br /> The article Sam Altman says scaling up compute is the "literal key" [...]

Match Score: 39.54

venturebeat
AI's GPU problem is actually a data delivery problem

Presented by F5As enterprises pour billions into GPU infrastructure for AI workloads, many are discovering that their expensive compute resources sit idle far more than expected. The culprit isn' [...]

Match Score: 38.85

venturebeat
Anthropic says its most powerful AI cyber model is too dangerous to release publicly — so it built Project Glasswing

Anthropic on Tuesday announced Project Glasswing, a sweeping cybersecurity initiative that pairs an unreleased frontier AI model — Claude Mythos Preview — with a coalition of twelve major technolo [...]

Match Score: 36.87