In a candid Core Memory podcast, Alexandr Wang explains why he left Scale AI for Meta, outlines the three guiding principles of Meta’s Superintelligence Labs, discusses compute stratification, evaluates the Muse Spark model as an appetizer, and argues that the AI endgame is far from over while stressing model welfare and safety.
DeepSeek’s valuation surged five‑fold to ¥350 B, securing a record ¥500 B financing round—40% of which comes from Liang Wenfeng’s personal ¥200 B pledge—while the company pivots toward heavy‑asset AI with new compute demands, talent challenges, and a V4.1 release slated for June.
Amazon has pledged an additional $5 billion to Anthropic, bringing its total investment to $13 billion and promising up to $100 billion in compute capacity over the next decade, while both firms and OpenAI race to diversify infrastructure, secure multi‑cloud partnerships, and out‑spend rivals in the accelerating AI arms race.
OpenAI’s unprecedented $122 billion financing round, combined with record user growth, soaring revenues, expansive compute investments, and a vision for an AI super‑app, illustrates a self‑reinforcing flywheel that is reshaping the AI industry and positioning the company near a trillion‑dollar valuation.
An analysis of six TIME magazine charts shows that while the United States maintains a lead in compute power and model performance, China leverages talent depth, abundant energy, and emerging chip access to narrow the AI competition gap.
The article analyzes OpenAI’s recent strategic partnerships and massive AI infrastructure investments, detailing multi‑gigawatt data‑center plans, chip collaborations, soaring energy demands, and the broader implications for AI as the next global infrastructure platform.
This article examines NVIDIA's Kepler GPU architecture, highlighting its 28 nm process, increased transistor count, expanded CUDA core count, PCIe 3.0 support, enhanced memory hierarchy, new compute units, scheduling improvements like Hyper‑Q, and performance metrics of the Tesla K20X, illustrating the substantial gains over previous generations.
This comprehensive guide walks you through 100 essential data‑center concepts—from basic definitions, tier standards, and modular design to networking layers, storage architectures, compute resources, security measures, operational practices, energy efficiency, emerging technologies, and industry ecosystem—providing a complete knowledge framework for modern digital infrastructure.
This article explains the essential OpenStack components—Keystone for identity, Glance for image services, Nova for compute, Neutron for networking, Placement, and related services—detailing their roles, interactions, and workflow, helping readers grasp how each piece fits into a functional cloud environment.
The article analyzes how generative AI models from GPT‑1 to the upcoming GPT‑5 are driving exponential growth in compute requirements, prompting massive cloud capital expenditures and intense competition among GPU vendors such as NVIDIA, AMD, Google, and emerging domestic chip makers, while also highlighting interconnect innovations and cost‑effective solutions.
This article details the evolution of the open‑source distributed database MatrixOne, describing its early NewSQL/MPP design, the scalability, performance and cost challenges it faced, and the architectural redesign that introduced decoupled compute, transaction, and storage layers with HTAP support using TAE, S3, and Logtail.
The article analyzes the AI compute ecosystem—highlighting GPUs as the core engine, network bandwidth as a bottleneck, and storage memory walls—while also promoting comprehensive server and storage e‑books for deeper technical insight.
The article analyzes how large language models like ChatGPT are reshaping the software ecosystem by positioning AI compute—driven by GPUs, high‑speed networking, and advanced storage solutions such as HBM and 3D‑stacked memory—as the foundational engine for future information systems, highlighting current market trends and technical challenges.
This article explains how enterprises can build a unified data storage and compute foundation, covering strategic goals, functional and architectural requirements, and the layered design of business support, storage‑compute, and resource management to enable scalable, secure, and high‑performance data platforms.
The article outlines the evolution of data‑center architectures, identifies capacity, efficiency, and performance challenges of traditional storage‑compute models, and presents the emerging Diskless architecture—leveraging DPU, CXL, RDMA, and high‑throughput networking—to achieve decoupled, pool‑based resources and improve overall data‑center utilization.
The whitepaper outlines the evolution and challenges of modern data center architectures, introduces the Diskless architecture that decouples compute and storage using high‑density storage, DPUs, RDMA and CXL, and details its design principles, key technologies, and industry implementations.
This article explains how to prevent UI blocking in Flutter by moving intensive calculations to background isolates using the compute helper and raw Isolate APIs, demonstrates code examples for task execution, progress reporting, and inter‑isolate communication, and discusses performance considerations and best practices.
The whitepaper introduces a comprehensive DPU performance benchmark methodology, detailing evaluation criteria across network, storage, compute, and security dimensions, and provides guidance on building test systems, selecting workloads, and designing metrics to enable fair, open, and objective assessment of diverse DPU products.
The article analyzes how hyper‑converged data center networks use lossless Ethernet to unify general compute, high‑performance computing and storage, enabling lifecycle automation, intelligent operation, and significant compute capacity gains without expanding server count.
This article examines how computing power has become a key production factor, detailing the shift from traditional CPUs and GPUs to specialized processors like DPUs, and explores emerging paradigms such as in‑memory, near‑memory, and edge computing that reshape data‑center architectures.
The article explains how read‑modify‑write race conditions can occur when updating a ConcurrentHashMap from multiple threads, demonstrates a failing JUnit example, and shows how using the atomic compute method with a lambda expression eliminates the race and ensures thread‑safe updates.
This article explains the definition and history of GPUs, why dedicated graphics processors are needed, how they are accessed through graphics libraries and vendor APIs such as OpenGL, DirectX, CUDA and OpenCL, and describes various GPU virtualization techniques including virtual graphics cards, passthrough, and vCUDA with their client‑server‑manager architecture.
The article presents a systematic, top‑down methodology for machine‑learning performance optimization—covering principles, benchmark‑driven loops, foundational hardware and software checks, profiling tools, throughput and latency metrics, and practical techniques for IO, compute, mixed‑precision, and distributed training to maximize resource utilization.
