Inference is the stage when a trained AI model uses new input to produce a prediction, classification, recommendation, or generated response. Training may take days across specialized hardware, while inference happens whenever a user submits a prompt, a camera captures an image, or an application requests a score. Production systems optimize inference for latency, throughput, memory use, energy consumption, and cost without degrading output quality. The model may run in a cloud service, data center, phone, vehicle, or edge device. Monitoring remains necessary because real-world inputs change, hardware can fail, and a model that performed well during evaluation may behave differently under live traffic.
Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’
OpenAI has announced a strategic partnership with Broadcom to design and deploy 10 gigawatts of custom AI accelerators, expanding its hardware ecosystem beyond Nvidia and AMD.
Google has unveiled Gemini Enterprise, a next-generation AI platform designed for business productivity and collaboration, positioning itself directly against Microsoft’s Copilot and OpenAI’s enterprise solutions in the race for workplace AI dominance.
Google Cloud and Figma have launched an expanded partnership to embed Google’s generative AI models – including Gemini 2.5 Flash and Imagen 4 – directly into Figma’s design tools, reducing latency and accelerating creative workflows.
Opera today introduced Neon, an AI-powered browser that can run tasks and navigate pages autonomously — aiming to shift web browsing from passive to “agentic.”
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.