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DeepSeek plans to train V5 model on Huawei Ascend 950 chips in 2027, extending DeepSeek-Huawei partnership from inference to full training.
Nvidia leverages industry relationships to influence US chip policy; OpenAI avoided public stance on China exports to avoid antagonizing Nvidia.
MediaTek receives $3.5B Nvidia and Alphabet convertible bond investment as it pivots from smartphones toward AI data center chips.
Premier Jung-tai Cho said on November 3 that Taiwan's government provided Nvidia with timely support in three areas—budget, legal frameworks, and policy—to establish its first overseas R&D center and headquarters in Taipei's Beitou-Shilin Technology Park (BSTP). Nvidia's decision has also become a key achievement for Taipei Mayor Wayne Chiang as he seeks reelection.
Nvidia CEO Jensen Huang's latest European trip has highlighted another strategic priority: using massive computing resources to simulate quantum systems and establish a foothold in the underlying quantum computing ecosystem.
US semiconductor intellectual property (IP) provider Ceva has expanded its NeuPro neural processing unit (NPU) portfolio into AI infrastructure for the first time. At a November 2 press conference in Taiwan, chief strategy officer Iri Trashanski said the company was not seeking to compete with Nvidia or other major chipmakers, but to meet demand for a common architecture spanning edge AI and cloud computing.
Nvidia verbally instructed cloud compute clients to limit China sales to <49% of services, tightening AI chip export controls.
Nvidia showcases AI and robotics partnerships at Seoul event with Samsung, SK Hynix, LG, Doosan, SK Telecom, Naver, Coupang.
In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as NCCL errors on other ranks, scheduler state... In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as NCCL errors on other ranks, scheduler state changes, and thousands of downstream messages. The reported exception shows where the workload stopped, not where the failure began. In this post, you’ll learn how to run NVIDIA Attribution on sample workload evidence… Source
As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery... As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery essential. A stalled training rank can trigger an NVIDIA Collective Communications Library (NCCL) timeout, a GPU node can stop responding to the scheduler, or a straggler can slow the entire job. Each disruption is manageable on its own… Source
Configuring and optimizing Kubernetes for new GPU hardware such as NVIDIA Vera Rubin is complex and error-prone. Drivers, kernels, runtimes, networking,... Configuring and optimizing Kubernetes for new GPU hardware such as NVIDIA Vera Rubin is complex and error-prone. Drivers, kernels, runtimes, networking, operators, and schedulers must work together, with settings tuned to the hardware and workload. Small mismatches can prevent workloads from running or limit performance. The NVIDIA AI Cluster Runtime (AICR)—part of the NVIDIA DSX AI Factory… Source
Physical AI developers working with world models face two distinct challenges: adapting models to their sensors, tasks, and data, and getting those models into... Physical AI developers working with world models face two distinct challenges: adapting models to their sensors, tasks, and data, and getting those models into applications reliably. NVIDIA Cosmos addresses each. Cosmos is a platform of world foundation models and development tools for physical AI. Cosmos Framework provides agent skills and recipes for world model training… Source
NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source,... NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source, modular software components built to support multi-tenant operations and scaling. Operating an AI factory requires a shared understanding of distributed workloads: their components, pod templates, replica counts, and status. Source
Anthropic securing 3.5GW+ compute capacity by 2026 via direct Nvidia and AMD procurement for infrastructure scaling.
Marvell acquires Lumentum for $115B to compete with Broadcom in optical networking; Nvidia holds stakes in both.
Iluvatar CoreX Tiangai 300 outperforms Nvidia H200 in select LLM inference, long-context, and MoE benchmarks.
Nvidia invests $1B in Naver, acquiring 4.54% stake and becoming third-largest shareholder.
FuriosaAI raises $620M at $2.9B valuation to develop AI accelerators competing with Nvidia.
Iluvatar CoreX Tiangai 300 and other Chinese GPUs target system-level AI optimization to compete with Nvidia H200 beyond raw compute.
LG Electronics, LG Innotek, and Hyundai Motor advance Nvidia robotics partnerships with data factory operations and autonomous driving infrastructure.
Nvidia explores cosigning financing for Nscale's West Virginia AI data center as investor appetite for AI capex cools.
Meta's Muse agent stack deepens Nvidia's role beyond GPUs into broader AI infrastructure as agent architectures grow more complex.
Teams building AI applications need a repeatable path from a model configuration to a working inference endpoint. NVIDIA Dynamo is a distributed inference... Teams building AI applications need a repeatable path from a model configuration to a working inference endpoint. NVIDIA Dynamo is a distributed inference framework that coordinates model serving across GPU workers. Deploying it on Kubernetes is only a first step. A successful model deployment requires more: GPU drivers, resource allocation, scheduling, networking, and monitoring must all work… Source
Building an autonomous agent requires a model, a harness, and domain-specific tools. To deploy an agent more securely, developers need a runtime that enforces... Building an autonomous agent requires a model, a harness, and domain-specific tools. To deploy an agent more securely, developers need a runtime that enforces policies, along with optimization and observability tools for reliable execution. For production agents, the challenge lies in making these agents capable and safe to operate with access to real enterprise systems and data. Source
NVIDIA GPUDirect Storage (GDS) accelerates GPU storage access in on-premises and cloud environments. It provides a robust, portable, and efficient data path... NVIDIA GPUDirect Storage (GDS) accelerates GPU storage access in on-premises and cloud environments. It provides a robust, portable, and efficient data path between NVIDIA GPUs and storage targets to increase bandwidth, minimize latency, and reduce CPU and memory utilization across a broad range of platforms, application frameworks, and workload patterns. Configuring NVIDIA GPUDirect Storage… Source
China dismisses US allegations of espionage targeting Nvidia CEO Jensen Huang as political smearing.
Nvidia secures substrate capacity across Taiwan, Japan, South Korea; FCBGA market projected at $27.3B by 2030.
Nvidia projects 69% market share in AI robotics chips by 2026, driven by software ecosystem advantage over Intel, Qualcomm, AMD.
Nvidia considers credit support for Nscale's West Virginia data center amid IPO pressure and Anthropic partnership.