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NVIDIA RTX Spark Powers Neural Rendering and AI Characters in Games

NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX...

Nvidia backs Indian startup helping companies run AI models with fewer GPUs

Simplismart says its annualized revenue run rate has grown 14-fold in six months, with the U.S. accounting for more than half its revenue.

Rokid has unveiled three new AI-powered wearable devices, expanding its display and interaction solutions.

On October 27, Rokid launched the AI glasses Rokid Glasses 2, the full-color AI glasses Rokid Color, and the interactive ring Rokid Ring.

The HKEX AI Index is here, and Southern Fund is the first to take the stage: How significant is this move?

Index implementation plus ETF authorization have provided Hong Kong‑listed AI and semiconductor stocks with new allocation tools and a fresh source of capital.

LooseLeaf: Enabling Serverless GPUs for Computational Notebooks

arXiv:2610.33275v1 Announce Type: new Abstract: Computational notebooks reserve a GPU for the whole session, yet the GPU computes only while a cell executes and sits idle otherwise. Serverless execution would release the GPU between cells, but reconstructing the accumulated notebook state on a fresh worker imposes substantial cost on every GPU cell. This paper presents LooseLeaf, a serverless GPU platform for interactive notebooks. LooseLeaf derives the state and import closures of each cell, the minimal variables and packages that the cell needs. Our analysis on 5,000 real-world notebooks shows that these closures are small, and user-cell editing leaves seconds to prepare them. LooseLeaf prepares a GPU worker with these closures while the user edits the cell. After execution, the worker s

LLMs in Cloud: A Discussion on Architecture, Orchestration, and Inference

arXiv:2610.33475v1 Announce Type: new Abstract: The widespread deployment of foundational Large Language Models (LLM) across cloud datacenters marks a fundamental paradigm shift from stateless deep learning toward distributed, memory-intensive generative workloads. However, serving massive foundation models under stringent Service Level Objectives (SLO) exposes critical physical and system bottlenecks. These challenges include the persistent High Bandwidth Memory (HBM) wall, operational asymmetry between compute-bound prefilling and memory-bandwidth-bound decoding, explosive Key-Value (KV) cache expansion, prohibitive serverless cold starts, and hardware reliability vulnerabilities. Existing literature reviews remain layer-siloed, decoupling low-level accelerator virtualization from cluste

DualPace: Decoupled Scheduling and Cooperative GPU--CPU Decoding for LLM Serving

arXiv:2610.33501v1 Announce Type: new Abstract: Large language model (LLM) serving relies on batching to improve inference throughput, but the key-value (KV) cache grows with sequence length and limits GPU-resident concurrency. CPU offloading can expand KV capacity using host memory, yet existing systems remain largely GPU-gated: CPU-resident requests make progress only when the GPU scheduler exposes suitable overlap opportunities. As a result, host memory and CPU compute cannot act as an independent serving resource, leading to bursty CPU-side progress and inflated tail inter-token latency (ITL) under load. We present DualPace, an LLM serving runtime that turns the host into an independently scheduled decode engine. DualPace separates scheduling authority across two engines: the GPU engin

ASRock Creator Radeon AI Pro R9700 Review: Workstation Inference, AMD Style

Today we are taking a look at ASRock's high-end Radeon AI inference card for workstations, the Creator Radeon AI Pro R9700. With 32GB of RAM and a $1700 price tag, the card is a highly affordable way to fill out a workstation for local AI inference The post ASRock Creator Radeon AI Pro R9700 Review: Workstation Inference, AMD Style appeared first on ServeTheHome.

SpaceX locks in $40B GPU financing deal with Apollo

SpaceX has locked in a binding commitment for roughly $40 billion in financing to pay for AI chips and related data center equipment, the company disclosed in a regulatory filing Monday. In a statement, SpaceX called it the “largest committed GPU financing” ever.

How NVIDIA Minerva Turns Telemetry Chaos Into Peak GPU Yield

AI Factories are some of the most technologically complex operational environments ever created. Hundreds of thousands of GPUs, NVIDIA Spectrum-X Ethernet...

Marvell’s early chipmaking gamble pays off amid AI demand

Eight years ago, chipmaker Marvell Technology Inc. decided to play leapfrog with nanometers, and its trajectory has been steadily upward since. In semiconductor manufacturing, nanometer designations identify generations of chipmaking technology, known as process nodes, rather than the exact physical dimensions of transistors. Advanced process nodes generally enable greater transistor density and improvements in performance […] The post Marvell’s early chipmaking gamble pays off amid AI demand appeared first on SiliconANGLE.

How NVIDIA Mission Control 2.5 Brings Autonomous Recovery to Kubernetes

Training large-scale AI models on thousands of GPUs is a high-stakes operation where monitoring and automation must be built-in to optimize cluster resiliency...

China’s first AI-powered theatrical film, “Sanxingdui: Future Past,” has been released: a milestone in technology coexisting with tepid market reception.

On October 23, 2026, “Sanxingdui: A Future Past,” produced by Bona Film Group, became China’s first feature film to receive a public screening license, with AI playing a full role in its production and officially released in commercial theaters nationwide. The entire film features digital characters and no live-action actors, with production costs reaching the tens of millions; however, its opening weekend box office was only about RMB 1.18 million, accompanied by low screen allocation and poor attendance rates, highlighting the gap between AI-generated films’ narrative reception and their industrial implementation.

Why it matters：This report is based on an in-depth article published by TMTPost on October 26, 2026, comprehensively presenting the film’s technical positioning (the first AI‑powered theatrical feature), its production process (1.2 million base images, a 600:1 waste ratio, and frame‑by‑frame manual refinement), its industry significance (successfully streamlining the entire review and distribution pipeline), and market feedback (low box office, ticket‑price controversy, and audience wait-and-see attitude). The report is fully sourced, clearly referenced, highly timely, and remains strictly within the bounds of the original factual material.

The compilation rules for four indices, including the Shanghai Composite Index, will be adjusted starting today; the State-owned Assets Supervision and Administration Commission has outlined plans to deepen state-owned enterprise reform; S&P’s new regulations will affect $375 billion in funds.

Starting October 26, 2026, the SSE Composite Index, the SSE A‑Share Index, the SSE Free‑Float Index, and the STAR 50 Index will officially implement their revised compilation methodologies, introducing a new cap of 15% on the weight of any single constituent. On October 23, the State-owned Assets Supervision and Administration Commission of the State Council emphasized the need to steadily advance the implementation of the latest round of state‑owned asset and state‑owned enterprise reform tasks. On the same day (October 25), S&P Dow Jones Indices announced adjustments to its growth/value stock classification criteria; the new rules will take effect on December 21, impacting approximately $375 billion in passive investment funds.

Why it matters：The three policy and market‑rule changes all exhibit significant timeliness and systemic impacts: the index‑weighting red line directly affects the stability of A‑share core benchmarks; the SASAC’s statement reflects the central government’s urgent plan to tackle tough challenges in state‑owned enterprise reform; and S&P’s rule overhaul reshapes the logic of value investing, with implications amounting to hundreds of billions of dollars—making it a key variable in the global capital markets. All information is drawn from an authoritative, comprehensive report published by TMTPost on October 25, 2026, with clearly verifiable timelines, entities, measures, and impact figures.

Aivres NVIDIA Vera Rubin and HGX B300 at OCP Summit

We check out the range of Aivres NVIDIA Vera, Vera Rubin, and Blackwell Ultra platforms at OCP Summit 2026 The post Aivres NVIDIA Vera Rubin and HGX B300 at OCP Summit appeared first on ServeTheHome.

Panel manufacturers are collectively raising prices to cope with upstream cost pressures driven by AI, but downstream acceptance remains low, putting pressure on end-product quality control.

Starting in September 2026, panel manufacturers such as TCL CSOT, BOE, and HKC have issued price‑increase notices one after another. The primary reason is the rapid growth of AI large models, which has led to a sharp surge in demand for HBM, squeezing capacity for mature‑process chips and driving up the prices of key components like driver ICs, PCBs, and glass substrates. Upstream suppliers, including Corning, have also raised their prices in tandem. Despite clear cost pressures, downstream TV and monitor brands have yet to broadly increase purchase prices, while end users continue to report frequent quality‑control issues such as overheating and black spots. As a result, these price hikes have not yet translated into actual sales or improved user experiences.

Why it matters：The materials system reveals the cross‑chain transmission mechanism through which the AI industry impacts indirectly related hardware sectors—rather than driving panel demand directly, it triggers a full‑chain cost repricing via the reallocation of wafer‑fabrication capacity. At the same time, it highlights a significant gap between price‑increase behavior and actual market feedback (price rigidity, declining shipments, and user complaints), underscoring the deep‑seated contradiction between industry bargaining power and product competitiveness.

Intel has received an exemption from the U.S. FCC’s ban on foreign-made robots, in order to ensure automated logistics at its chip fabs.

On October 22, 2026, the U.S. Federal Communications Commission (FCC) granted Intel a one-year exemption, allowing it to continue using certain foreign‑made automated guided vehicles (AGVs) at its U.S. semiconductor fabs, as the original ban’s vague wording could inadvertently affect critical semiconductor manufacturing equipment.

Kazakhstan is accelerating the development of its AI computing infrastructure, and Firebird has obtained U.S. export approval to deploy 45,000 NVIDIA Grace Blackwell GPUs.

Kazakhstan’s AI ecosystem is transitioning from the planning phase to large-scale deployment. Firebird has obtained U.S. export licenses to build an AI factory in Kazakhstan, with the first phase deploying 45,000 NVIDIA Grace Blackwell GPUs. Kazakhtelecom has already brought online more than 1,000 H200/B300 GPUs and plans to add over 2,000 Vera Rubin and other GPUs by 2025. Multiple stakeholders are collaborating to advance computing power supply, developer access, and the local implementation of AI applications.

Amazon SageMaker AI has launched GPU-free managed model training sessions, now generally available.

AWS has announced that SageMaker AI’s serverless model customization feature now includes “Training Sessions,” which are fully available starting today. Users can independently define post-training logic—such as custom loss functions, reward signals, or multi‑round reinforcement learning—while the platform automatically handles GPU allocation, distributed execution, fault tolerance, and checkpoint storage.

Amazon SageMaker launches the Training Session API: users can write custom training loops for large models without managing GPUs.

Amazon SageMaker has released the Training Sessions API, enabling users to implement custom large-model training workflows—such as SFT, RLHF, GRPO, and KTO—using pure Python scripts. GPU resource scheduling, fault tolerance, distributed execution, and checkpoint management are all handled by the service.