wccftech.com — Long-term TSMC contracts rarely free up capacity, but cheaper ASICs such as Google's TPUs could gain volume as US scrutiny of NVIDIA grows.
finance.yahoo.com — Alphabet introduced two new chips this morning.
www.unite.ai — Google Cloud on November 9, 2026, announced that Cloud Pathways is generally available on Google Kubernetes Engine (GKE), bringing a single-controller runtime for large-scale training and inference to customers. Google s...
www.thestack.technology — They tackle two enterprise AI concerns: scaling agents eats up memory, and private data needs confidential computing.
www.datacenterdynamics.com — Successful applicants will be able to test and evaluate their AI models on Q.ANT’s photonic hardware
cloud.google.com — Compared to creating a static nodepool for each TPU slice, GKE dynamic slicing reduces AI workload startup and workload recovery times.
www.infoq.com — At the recent PyTorch Conference North America 2026, Amazon introduced TorchNeuron, a native PyTorch backend that enables developers to run existing PyTorch code on AWS Trainium AI chips with virtually no code modifications.
www.unite.ai — NVIDIA announced on November 8, 2026, a collaboration with Rebellions and SK Telecom to connect Rebellions’ next-generation R200 processors to NVLink Fusion for Korean sovereign AI infrastructure, with the resulting syst...
wccftech.com — DFRobot’s DX-M1 packs 25 TOPS of INT8 inference into a $139 M.2 stick, sipping just 5W while delivering dedicated 4GB LPDDR5 edge AI power
semafor.com — According to a PowerPoint presentation reviewed by Semafor, former Intel CEO Pat Gelsinger said the industry is failing to innovate on new technologies that could create exponential performance gains.
sdxcentral.com — An 8×8 optical circuit switch demonstrator reports low optical loss, with customer samples preceding planned commercial products
cerebras.ai — Learn how Cerebras solved wafer-scale computing’s toughest engineering challenges and why its AI systems are built for long-term reliability.
Tech Xplore — Artificial intelligence is increasingly pervasive and capable but is also very hungry for power. Researchers, including those at the University of Tokyo, have developed a new approach to building physics-based AI—systems that use the natural behavior of physical systems to process information—that dramatically reduces the amount of computation required for them to run. The approach simplifies both how these systems learn and how they are structured. It was demonstrated using an optical computing system that processes information with light and could help pave the way toward faster and more energy-efficient AI hardware.
CIO — As nations invest billions in localized computing infrastructure and regional models, enterprise leaders must weigh compliance, security, and cloud scalability to future-proof their tech stacks.
datacenterdynamics.com — Server maker will initially purchase 10,000 SN50 chips from SambaNova
investingLive — AMD shares rose after hours as Q3 revenue of around $13.4bn and adjusted EPS of about $2.10 beat forecasts. It guided Q4 revenue to around $17bn, above the…
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