Nvidia

396 stories

Nvidia is a US chip company that designs GPUs and AI computing platforms, and its CUDA software stack is widely used to train and run AI models. Recent coverage has centered on its stock and valuation: Apple overtook Nvidia as the world's most valuable company on Oct. 30, and Nvidia shares fell as much as 5.3% that day, with FQ2 revenue of $96 billion, up 106% year over year. Nvidia also plans a $1 billion investment in Naver for a 4.54% stake.

AI memory demand surges, Micron stock may rally

Micron, a leading maker of AI memory chips, has seen its stock soar 225% this year but trades about 24% below its all-time high. Amazon raised its 2026 capex to $220 billion due to higher memory costs, and Alphabet lifted its forecast to $195-205 billion. The global memory shortage could dominate earnings discussions and drive Micron shares higher.

The Motley Fool ·

Nvidia backs Simplismart in $14M Series B round

Indian startup Simplismart raised a $14M all-primary Series B led by Dallas Venture Capital, with Nvidia, Accel, Shastra VC and Micromax participating, valuing it at about $100M. Its software optimizes model deployment so companies run AI with fewer GPUs and lower cost; annualized revenue grew 14-fold in six months, with the U.S. contributing over half.

TechCrunch ·

Nvidia invests $3B in Meta as Meta buys more Nvidia chips

Nvidia will invest $3 billion in Meta's planned equity offering, the Wall Street Journal reports, as the two expand their partnership around Meta's AI product Muse. Meta plans to run next-gen AI agents on Vera Rubin systems and Vera CPUs, and aims to raise up to $95 billion via stock offerings for AI infrastructure. AMD and Broadcom signaled interest in up to $5 billion combined.

The Decoder ·

Practical notes for GPU kernel dev setup

An r/MachineLearning post collects setup pitfalls for GPU kernel development: CUDA 13 drops Maxwell, Pascal and Volta, so toolkits must match compute capability; PyTorch's bundled CUDA runtime says nothing about nvcc availability; and GPU launches are async, so naive timing measures queueing and requires warmup, sync and CUDA events.

r/MachineLearning ·

AI Debt Monster: Hidden Risks in Infrastructure Spending

AI uncovered a PHP vulnerability affecting 70% of the internet but also fueled exploits, highlighting a paradox. However, the bigger risk is AI infrastructure debt: $3.5 trillion in global spending by 2030, with US hyperscaler capex hitting $800 billion in 2026. Hidden commitments and circular investments could trigger an 'AI Enron' financial crisis.

WhoWhatWhy ·
Load more