…strong demand is driven primarily by Agentic AI, which has led to a significant increase in data center deployments housing several Petabytes of data. Enterprise alone now accounts for 43% of the…
…Based on the new OpenCore architecture, the GPU is designed for AI Training, Large-Scale Science Workloads for HOPC, gaming (Cloud Computing), and more. The company isn't unveiling a whole lot…
…As we know, Matrix Multiplication is the core block of neural networks and LLMs in AI workloads. Current SIMD (Single Instruction, Multiple Data) extensions, such as AVX10, can do matrix multiplication, but…
…DLSS 4.5's 2nd Gen Transformer Model Improves Everything For Super Resolution, Available On All RTX GPUs NVIDIA has been extensively training its DLSS model on its advanced data centers featuring…
…keep pace with the ever-increasing memory bandwidth demands of Artificial Intelligence (AI), High-Performance Compute (HPC), and other data center applications. Renesas has been instrumental in the design, development, and deployment…
NVIDIA Blackwell Ultra goes official , offering a huge scale-up in memory capacity with upgraded AI compute capabilities for data centers. NVIDIA Blackwell Gets Bigger With "Ultra" Variant, Packs 50% More Performance…
…HBM4E Drives A 33% Die Density Uplift, and a 38% Boost in Memory Bandwidth, Setting Eyes at 4 TB/s for Next-Gen AI Datacenter Chips NVIDIA and AMD are going to…
…As AI, data processing, and everyday computing grow more demanding, extensions like APX ensure that x86 will keep delivering better performance and efficiency for years to come. In a world that increasingly…
…This is due to strong demand for data centers and AI servers. AMD's server market share increased to 28.8% in Q4 2025, versus 25.2% in Q4 2024, while Intel…
…NVIDIA Wants Everyone To Rethink AI TCO With "Cost Per Tokens" Metric Tokens are the single most important metric for AI. While yesterday's data centers were evaluated on their raw computing…
