Google LLC introduced two new custom silicon chips for artificial intelligence today at Google Cloud Next 2026, unveiling two ...

Google's 8th-gen TPUs split training and inference into two chips. Here's what it means for enterprise AI infrastructure ...

FriendliAI, The Frontier AI Inference Cloud, is collaborating with Samsung SDS, a leading GPU infrastructure-as-a-service (IaaS) provider in South Korea, to deliver frontier model AI inference ...

EcoHash Technology LLC, the dedicated HPC and AI inference subsidiary of Cango Inc. (NYSE: CANG), launched its public digital ...

Google is packing ample amounts of static random access memory into a dedicated chip for running artificial intelligence ...

“I get asked all the time what I think about training versus inference – I'm telling you all to stop talking about training versus inference.” So declared OpenAI VP Peter Hoeschele at Oracle’s AI ...

NVIDIA Dynamo 1.0 provides a production-grade, open source foundation for inference at scale. Dynamo and NVIDIA TensorRT-LLM optimizations integrate natively into open source frameworks such as ...

The AI hardware landscape continues to evolve at a breakneck speed, and memory technology is rapidly becoming a defining differentiator for the next generation of GPUs and AI inference accelerators.

Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost margin.

Roman Chernin is the CBO and cofounder of AI infrastructure company Nebius. His career spans over 20 years in the tech industry. Every major advance in AI begins with model training, but the ...