Elon Musk dissolved xAI, rebranded it as SpaceXAI, and granted Anthropic access to the 220,000‑GPU Colossus 1 supercomputer, a move framed as a strategic strike to undermine OpenAI by leveraging massive orbital‑grade compute power.
This guide walks you through installing NVIDIA drivers and Docker, configuring the NVIDIA Container Toolkit, and deploying GPUStack in Docker to manage heterogeneous GPU resources, run large language, multimodal, diffusion, and embedding models, and scale from a single node to a multi‑node GPU cluster.
This guide walks you through installing NVIDIA drivers, Docker, and the NVIDIA Container Toolkit, then shows step‑by‑step how to run GPUStack in Docker, expand a GPU cluster, and serve large language, multimodal, diffusion, and embedding models with OpenAI‑compatible APIs.
This article explains how Baidu’s Kunlun supernode, built on high‑density liquid‑cooled cabinets and a modular 1U 4‑card design, breaks traditional 8‑card limits, boosts compute density four‑fold, improves power and cooling efficiency, and provides a scalable foundation for large‑model AI training and inference.
The article provides a detailed technical analysis of Huawei's CloudMatrix 384 AI supernode—its 384 Ascend 910C chips, 300 PFLOP BF16 performance, massive memory and bandwidth, power consumption, scale‑up and scale‑out optical networking, and how it compares to Nvidia's GB200 NVL72 in architecture, cost, and energy efficiency.
This guide explains how to use the AIBrix distributed inference platform to deploy the massive DeepSeek‑R1 671B model across multiple GPU nodes, covering cluster setup, custom vLLM images, storage options, RDMA networking, autoscaling, request handling, and observability, turning a weeks‑long deployment into an hour‑scale process.
The article analyzes Elon Musk’s Colossus AI supercomputer—its 100,000 NVIDIA H100 GPUs, record‑fast 122‑day construction, vertical‑integration strategy, and the broader implications for U.S. AI infrastructure dominance and China’s competing challenges in funding and chip supply.
The article details the design and deployment of 360's AI Compute Center, covering GPU server selection, high‑performance networking, Kubernetes‑based cluster management, advanced scheduling, training and inference acceleration techniques, and a comprehensive AI development platform with visualization and fault‑tolerance features.
This article details the design and implementation of 360’s AI Computing Center, covering server selection, network topology, Kubernetes scheduling, training and inference acceleration, and the AI platform’s core, visualization, and fault‑tolerance capabilities for large‑scale AI workloads.
This article analyzes the challenges and solutions for constructing a super‑large GPU training cluster, outlining five fundamental design principles, a four‑layer plus one‑domain architecture, and practical considerations for hardware, networking, and operational reliability in AI workloads.
This article analyzes the architectural upgrades required for ultra‑large AI clusters, covering single‑GPU performance, super‑node scaling, DPU‑based heterogeneous computing, power‑efficiency, high‑throughput storage, and robust high‑speed networking to support trillion‑parameter model training and inference.
The article details Baidu's development of a massive high‑performance GPU/IB cluster, its architectural design, the challenges of training trillion‑parameter models, and how the integrated AI stack—spanning hardware, framework, and resource management—overcomes compute, memory, and communication bottlenecks to accelerate large‑model training.
Baidu’s AI infrastructure combines a massive InfiniBand‑linked GPU cluster, Kunlun chips, the PaddlePaddle framework, and the Wenxin model suite with 4D hybrid parallelism, elastic fault tolerance, and a two‑stage training pipeline to overcome computation, memory, and communication walls, delivering world‑leading MLPerf performance for large‑scale LLMs.
Tencent Cloud's new HCC high‑performance computing cluster triples previous generation performance with 3.2 TB/s server bandwidth, Xingsha servers and NVIDIA H800 GPUs delivering up to 1979 TFlops, while its Xingmai 3.2 T ETH RDMA network, TB‑level storage via COS + GooseFS, and multi‑form access (bare metal, cloud servers, containers, functions) enable efficient large‑model training.
This article details Weibo's multimodal content understanding platform, covering its massive data challenges, heterogeneous model support, standardized pipelines, platformization, workflow architecture, GPU heterogeneous cluster management, resource scheduling, performance optimization, and full‑stack monitoring to achieve stable, low‑latency AI services at scale.
