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Oct 27Tue
  1. PyTorch Blog34

    Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch

    This PyTorch blog case study takes the multilingual Nemotron 3.5 streaming ASR model from PyTorch to ExecuTorch and profiles it on a Google Pixel 11, measuring how chunk size and precision affect streaming WER and processing time. Enabling Arm SME2 cut whole-recording processing time from 9.79 to 4.61 seconds for FP16 (2.12x) and from 5.20 to 3.24 seconds for QC8W (1.61x), with GEMM driving most of the gain, while longer chunks improved accuracy at the cost of latency. ETDump and Arm Streamline were combined to attribute speedups to operators and confirm which KleidiAI SME2 microkernels actually ran.

  2. AWS Machine Learning Blog38

    Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

    This post shows how to build enterprise agentic retrieval on a Managed Amazon Bedrock Knowledge Base using the AWS CDK in Python, deploying four stacks (knowledge bases, AgentCore Gateway, agent runtime, and dashboards) with one `cdk deploy --all` command. It contrasts classic single-shot RAG with agentic retrieval, where an agent on AgentCore Runtime routes questions across two managed knowledge bases (financial and weather) exposed as MCP tools through the Gateway, invoking the AgenticRetrieveStream API for grounded, cited answers. The solution also provisions two CloudWatch dashboards spanning seven telemetry layers plus on-demand and continuous evaluation, since agentic systems need observability to verify behavior.

  3. AWS Machine Learning Blog40

    Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon Bedrock

    This post explains how AI agents resend their full context (tool definitions, instructions, and history) on every turn, and how Amazon Bedrock prompt caching avoids recomputing that prefix by reusing saved key-value state. It covers implicit versus explicit caching, caching conversation history and tool results, handling tool changes with allowed_tools and additional_tools, sharing cached prefixes across subagents and new sessions via prompt_cache_key, and monitoring cache reads and writes through response usage and CloudWatch.

  4. AWS Machine Learning Blog34

    Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

    This post updates a 2023 MLOps pipeline pattern for visual quality inspection to Amazon SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization. It describes a dual-branch SageMaker Pipeline that trains an anomaly detector and a defect classifier per camera, gates models on their exported ONNX artifacts, compiles them with Neo, and registers them with camera metadata, while a Step Functions and DynamoDB semaphore layer paces fleet training. The sample deploys via AWS CDK and a full synthetic-data run costs about $0.07 in compute.

  5. Databricks Blog40

    Building live intelligence: Databricks AI Search supports Streaming Tables, Materialized Views, and Managed tables using Iceberg

    Databricks AI Search now natively supports Streaming Tables, Materialized Views, and Managed tables using Iceberg v3+ in addition to Delta tables, letting users index live events, continuously maintained personalized state, and open-format data directly without separate conversion and synchronization pipelines. Streaming Tables keep indexes fresh as new records arrive, Materialized Views propagate incrementally maintained aggregated or joined state for personalized search, and Managed Iceberg tables serve as sources without conversion or migration. Index creation follows the same Delta Sync index workflow via UI or API, eliminating the intermediate Delta table and custom sync pipeline.

  6. Sierra Blog22

    Sierra is on Google Cloud Marketplace

    Sierra is now available on Google Cloud Marketplace, letting companies use existing Google Cloud commitments to build and deploy AI agents, with payment based on outcomes delivered rather than tokens consumed. Qualifying Marketplace purchases count toward Google Cloud commitments, with unified billing and Private Marketplace governance in the Google Cloud Console. Sierra says nearly 50% of the Fortune 50, 1 in 3 leading banks, and 5 of the 10 largest healthcare companies work with it.

  7. Meta Engineering40

    Meta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks

    At the 2026 OCP Global Summit, Meta presented networking work supporting its AI models and gigawatt-scale Prometheus cluster, including the liquid-cooled IcePack/IceCube rack with 102.4T switch blades, the Flattened Planar Fabric for scale-out, BAG super-spine and SRv6 backbones for scale-across, and Tahan, an Ethernet scale-up switch for MTIA 300. Meta also introduced OSPA, an OCP initiative co-led with Microsoft and Alibaba to standardize the switch platform layer with a v1.0 spec submitted to OCP, and said its MetaRoCE RDMA transport is going open source, starting with a user-space version on GitHub. Recorded talks and slide decks from the event are available on the 2026 OCP Global Summit website.

Oct 24Sat
  1. AWS Machine Learning Blog58

    Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide

    This AWS post walks through an end-to-end Multi-Turn Reinforcement Learning pipeline on Amazon SageMaker AI, using a Wordle agent hosted on Amazon Bedrock AgentCore that reports one outcome reward per episode for GRPO training of a LoRA adapter. In the example run, gpt-oss-20b improved from 61% to 84% solve rate on held-out words at default reasoning effort and from 15% to 69% at low effort, for about $339 in training tokens. It also covers IAM roles, dataset preparation, MLflow monitoring, serving-setting evaluation, cleanup, and reward-design mistakes and training failures encountered.

  2. Google Research62

    Bringing foundational models to the front lines of TB screening

    Google Research and Google for Health describe their path from a 2019 Radiology benchmark showing deep learning could match radiologists in detecting active pulmonary TB to prospective multi-site validation, CE MDR Class IIb certification via Nexus AI, and deployment in about 40 facilities across six countries, where over 90 clinicians have evaluated more than 25,000 chest radiographs. They also detail offline AI screening four kilometers underground at South Africa's Mponeng Gold Mine and open models (HAI-DEF, MedSigLIP, MedGemma, HeAR), including Indonesia's Ministry of Health fine-tuning MedGemma and MedSigLIP on domestic chest radiographs for a national TB program.

    Why it matters: It offers a concrete case study of moving medical AI from benchmark to certified, field-deployed tools, with named partners, regulatory milestones and deployment numbers.

  3. AWS Machine Learning Blog34

    How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations

    Bayer Crop Science built the Agentic Decision Engine (ADE), a multi-agent natural language interface over its Connected Decision Engine, running on Amazon Bedrock AgentCore with LangGraph orchestration and 22 specialist agents. In its first phase, ADE resolves about 80 percent of user questions without an engineer in the loop, and exposing APIs through AgentCore Gateway cut per-API integration overhead by more than 99 percent. The post details five design decisions, production readiness work such as task-level observability and cost attribution, and ongoing operations with AWS Unified Operations.

  4. AWS Machine Learning Blog22

    Orchestrating SageMaker AI endpoint auto scaling with AWS CDK

    Updating a SageMaker AI real-time endpoint configuration fails through CloudFormation or CDK because SageMaker AI requires the scalable target to be deregistered first, and manual deregistration leaves CloudFormation unable to track Application Auto Scaling resources. The post presents a reusable CDK construct that replaces the native scalable target and scaling policy resources with three custom resources—Deregister, Register Scalable Target, and Attach Scaling Policy—ordered with explicit dependencies and driven by a ConfigHash so endpoint and auto scaling changes deploy together. It includes a walkthrough using the sample-sagemaker-endpoint-autoscaling GitHub repository and guidance on integrating the construct into an existing stack.

Oct 23Fri
  1. Hugging Face Blog38

    Does Your Model Run on AWS Trainium? Look It Up

    Hugging Face introduces a Space that checks whether popular Hub models run unmodified on AWS Trainium via the torch-neuronx backend, reporting sharding, wall time, and peak memory, and providing the run.py script used. Results come from the public aws-neuron/torch-neuronx-model-support dataset, with one Parquet file per neuronx-cc and library version pair. Tests currently cover text-generation and image-text-to-text Transformers models fitting on at most 4 Trainium2 devices (16 cores), generating 32 tokens.

  2. AWS Machine Learning Blog18

    How U.S. Hunger connects leaders and data using Amazon Quick

    U.S. Hunger, which reports delivering more than 250 million meals, uses Amazon Quick to let CEO Rick Whitted explore Full Cart datasets directly and to give 17 colleagues across leadership, technology, finance, and compliance shared Spaces for KPI definitions, financial planning, and research. Data from its Impact Manager platform is refreshed from SQL Server into SPICE nightly at 2 AM ET, and the organization plans a Q4 2026 enterprise KPI framework routing NetSuite, Salesforce, and other systems through Boomi into Amazon Redshift.

  3. AWS Machine Learning Blog34

    Train Qwen3 on AWS Trainium with torchtitan

    This post walks through training Qwen3 three ways on a single trn2.48xlarge using torchtitan-neuron: via Hugging Face Transformers v5 as a modeling backend, via torchtitan's native Qwen3 by changing config.model_spec, and via NKI mixture-of-experts kernels from the torchtitan-neuron recipes, all using the same torchrun launch pattern. It also shows how to load and save published safetensors checkpoints, attach a custom NKI rope_hf kernel through torchtitan's override registry, and scale with data and tensor parallelism across 64 logical NeuronCores.

  4. AWS Machine Learning Blog38

    Adaptive agentic intelligence framework using Strands Agents

    The Adaptive Agentic Intelligence Framework (AAIF), built on Strands Agents, uses a small local language model (llama3.2:1b) as an orchestrator that routes routine requests to local SLMs via Ollama and complex requests to cloud models through Amazon Bedrock. The post walks through an IoT monitoring reference implementation with tools for device telemetry, local vision analysis, and Bedrock-based expert insights, and describes cloud-only, hybrid, and air-gapped deployment configurations. It also outlines converting tools into MCP servers for distributed tool calling and applying Amazon Bedrock Guardrails to the cloud inference path.

  5. AWS Machine Learning Blog58

    5 production challenges in multi-agent systems and how Deep Insight solved them

    AWS's Solutions Architecture team describes Deep Insight, an open-source multi-agent system (Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker) that turns a CSV file and a natural-language prompt into a validated DOCX analysis report with charts. The post, part one of three, maps five production challenges to a stack: Strands Agents SDK Graph and Agents-as-Tools patterns for execution flow, role-specific Claude models on Amazon Bedrock, Amazon Bedrock AgentCore for managed deployment, AWS Fargate plus Application Load Balancer for isolated code execution, and DynamoDB, Amazon SNS and Amazon Cognito for monitoring. It also cites LG Electronics' ChatInsight and TechRecon as reuse of the same architecture.

  6. AWS Machine Learning Blog20

    Securing Quick access from corporate networks with AWS PrivateLink

    This walkthrough explains how to connect on-premises data sources to Amazon Quick privately using AWS PrivateLink VPC endpoints and Quick VPC connections, so traffic stays off the public internet over Direct Connect or Site-to-Site VPN. It covers endpoint creation for website and API access, DNS resolution via Route 53 private hosted zones, endpoint policies, execution roles, data source VPC endpoints, required external domains, and enforcing VPC endpoint restrictions. It also addresses identity federation with IAM Identity Center and notes that browser sign-in still requires outbound access to public AWS sign-in endpoints.

  7. NVIDIA Blog30

    Kazakhstan Expands Compute for Its AI Future

    During Jensen Huang's visit to Astana, Firebird signed an investment agreement with Kazakhstan securing a U.S. export license for 45,000 NVIDIA Grace Blackwell GPUs, with a first phase under construction with Kazakhtelecom at Data Center Valley targeting commercial launch in Q3 2027. Kazakhtelecom has already deployed 400 NVIDIA H200 and 480 NVIDIA B300 GPUs in Astana and Almaty, and NVIDIA announced an investment in Kazakhstan-founded startup Higgsfield AI. Huang and Deputy Prime Minister Zhaslan Madiyev discussed Kazakh-language models, physical AI for mining and agriculture, and AI security via NVIDIA OpenShell at the Alem.AI fireside chat.

  8. AWS Machine Learning Blog43

    Amazon Polly delivers a major upgrade to its Generative engine

    Amazon Polly's upgraded Generative engine is now generally available, offering more expressive and accurate speech with low latency as a drop-in upgrade that requires no code changes. It adds 14 new voices across 9 locales, including the first generally available Generative voices for Hindi and Finnish, bringing the catalog to 57 voices across 11 languages and 25 locales, with expansion to AWS GovCloud (US-West). Pricing is unchanged, and the engine continues to support SSML and the Bi-Di Streaming API.

  9. AWS Machine Learning Blog31

    How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases

    IskayPet, an Iberian pet care company with over 330 stores and 4,500 employees, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases that answers employee questions across retail operations, veterinary care, people and onboarding, and procurement in four languages and three countries without managing RAG infrastructure. The solution uses the native SharePoint connector with advanced indexing, a dedicated knowledge base per assistant scoped to specific sites or libraries, document-level access control tied to existing SharePoint permissions, and hybrid search with reranking. Built with Kiro and Claude Haiku 4.5, it reportedly cut time to find an operational answer from 3–5 minutes to about 7 seconds in roughly the first two months in production.

  10. AWS Machine Learning Blog58

    Announcing new ways to customize models with SageMaker AI, without managing GPUs

    AWS announced the general availability of training sessions in Amazon SageMaker AI serverless model customization, letting customers write and coordinate their own post-training loop, including custom loss functions and reward signals, while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. The sessions support techniques such as SFT, DPO, RLVR, RLAIF, and multi-turn RL, with LoRA or full fine-tuning, and are available with support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Customers can start via the SageMaker Python SDK and cookbook or in SageMaker Studio, with data kept in their own S3 buckets and IAM, KMS, and VPC controls.

  11. AWS Machine Learning Blog58

    Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs

    Amazon SageMaker AI introduces training session APIs that let you write a custom training loop in Python on your local machine while SageMaker runs GPU-bound operations such as forward_backward(), optim_step(), and sample() on managed accelerators with your base model and LoRA adapter. The APIs are organized around ServiceClient, TrainingClient, and SamplingClient in the SageMaker Python SDK, and the post walks through a minimal GRPO loop on GSM8K (held-out accuracy rising from 56% to 75% in 10 steps) plus a Code RL recipe on competitive programming problems (held-out accuracy from 40% to 50.5%). A companion cookbook provides ready-to-run SFT and RL recipes.

  12. AWS Machine Learning Blog34

    Deploy AI in a HIPAA-eligible environment with Amazon Quick

    This AWS post explains how to deploy Amazon Quick, a HIPAA-eligible service, for healthcare workloads that touch protected health information, covering covered entities and business associates, the distinction between HIPAA-eligible and HIPAA-compliant, and the unstructured (knowledge base) and structured (Amazon Quick Sight) PHI data paths. It details the controls Amazon Quick provides—managed infrastructure, encryption, RBAC and SSO, CloudTrail and CloudWatch vended logs—alongside what it does not do, such as de-identification and lake-level governance handled by AWS Glue, Glue DataBrew, and Lake Formation. It closes with a fictional health plan walkthrough, irreversible day-zero decisions (customer managed key, identity model, ACL-aware knowledge bases), and a step-by-step implementation guide.

  13. AWS Machine Learning Blog38

    Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI

    This AWS post describes streaming each sentence an agent produces to a hosted Kokoro-82M text-to-speech model on a SageMaker AI real-time endpoint, so synthesis starts on the first sentence instead of waiting for the full response. It covers bidirectional WebSocket handling, an incremental sentence chunker, and two transports for returning audio to the browser, reporting that incremental sending cut mean time to first audio byte from 329.3 ms to 156.7 ms in isolation and made bidi mode 2.4 to 3.5 times faster than batch in a deployed agent.

  14. AWS Machine Learning Blog32

    Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

    This AWS post deploys Kokoro-82M, an 82-million-parameter non-autoregressive TTS model, as a custom container on an Amazon SageMaker AI real-time endpoint via AWS CDK stacks, then replaces a guessed auto scaling target with values derived from two live measurements: Q_max (the highest per-instance concurrency that still meets a 3,000 ms p95 time-to-first-audio SLO, measured at 50) and T_total (end-to-end scale-out time, measured at 260.2 s and planned against 320.2 s). Scaling uses the native ConcurrentRequestsPerModel metric, and a five-command tool chain (drift, qmax, ttotal, plan, validate) computes the scale-out threshold (37.5), scale-in threshold (25.0) and asymmetric cooldowns by arithmetic. The authors note the methodology generalizes to other latency-sensitive, concurrency-bound real-time SageMaker AI endpoints, while the specific numbers hold only for Kokoro-82M on ml.g5.xlarge.

  15. AWS Machine Learning Blog38

    How Rescale built an AI triage agent with Strands Agents and AI-DLC

    Rescale used the open source AI-Driven Development Lifecycle (AI-DLC) framework on AWS to build an agent that classifies and routes nightly end-to-end test failures, going from problem definition to staging in five days and to production within weeks. The agent runs in Rescale's Argo Workflows CI/CD pipeline on Amazon EKS, uses Claude on Amazon Bedrock for classification, retrieves historical failure patterns from a knowledge base, and queries pod logs via MCP, producing structured remediation reports. In production, classification agreement with human triage rose from 44% to 68%, and time blocking production deployment while failures are analyzed fell 20%.

Oct 22Thu
  1. NVIDIA Blog60

    NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories

    NVIDIA launched Minerva, an AIOps platform that combines telemetry from infrastructure and AI workloads and applies purpose-built AI models to surface timely, actionable insight. It builds a dynamic knowledge graph mapping GPUs, SuperNICs, switches, cables and the jobs running on them, analyzes billions of data points per hour to detect anomalies and predict failures, and includes a native AI assistant plus connections to external agentic AI tools. IREN is among the partners using Minerva, and Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy it, with Dell Technologies, Lenovo and Supermicro offering it in their AI factory operations portfolios.

    Why it matters: It details a new NVIDIA observability platform and its named deployment partners, useful for readers tracking AI data center operations tooling.

  2. NVIDIA Blog40

    Leading European Financial Services Organizations Embrace Open Models to Build Proprietary Intelligence

    At GTC Berlin, European banks, payment companies and data providers showcased production-scale AI built on NVIDIA accelerated computing and open weight models running on their own infrastructure, including Revolut's PRAGMA foundation model serving an initial 80 million customers and Starling Bank's agentic model trained on U.K. sovereign cloud. Open weight models let institutions like bunq, NatWest, Modulr and Santander build proprietary intelligence at controlled cost, while LSEG, RavenPack, HSBC and Stripe reported speedups such as 10x faster tick-data analysis and 42x faster data pipelines for fraud detection.

  3. NVIDIA Blog36

    How Companies Are Using NVIDIA DSX to Break the Power Bottleneck

    NVIDIA DSX AI Factory Platform lets companies plan, simulate and run the grid and AI factory as one system, addressing power bottlenecks such as over $300 billion in backlogged equipment and over 200 gigawatts of projects in U.S. interconnection queues. The platform combines DSX Sim, DSX Flex, DSX MaxLPS and DSX Exchange, with partners including GE Vernova, Hitachi Energy, Schneider Electric, Siemens Energy, Phaidra and Emerald AI. Phaidra reports about 10% more compute within the same power envelope, and Emerald AI's approach is modeled to unlock up to 100 gigawatts on the existing U.S. grid.

  4. NVIDIA Blog25

    NVIDIA Transforms European Retail, From Digital Discovery to Physical Operations

    European retailers are using NVIDIA-accelerated open models and physical AI across commerce and fulfillment, including Shopify post-training NVIDIA Nemotron 3.5 Lightning for its Sidekick GraphQL subagent, which it says surpasses a leading frontier model in accuracy on an internal benchmark while cutting serving costs by 90% and delivering responses 7x faster. Otto Group extended its autonomous warehouse initiative, built with Reply on NVIDIA Omniverse and running on Google Cloud, to its largest fulfillment center in Haldensleben, Germany, and Nebius is showcasing three early-access NVIDIA retail blueprints at GTC Berlin. Other cited deployments include Zooplus with Infosys, Photoroom with Vestiaire Collective, L'Oréal with Omi and NVIDIA ALCHEMI, Mirakl, ALLSIDES, and CATCHES with AMIRI.

  5. Hugging Face Blog75

    NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

    NVIDIA introduces Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases via in-context learning in a single forward pass, requiring no task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and is available through NVIDIA's open-source structured-data-models and kumo-engine libraries or as an NVIDIA NIM.

    Why it matters: Useful for ML teams weighing whether to replace production XGBoost pipelines with a pre-trained relational model, though the reported gains come from NVIDIA's own paper and benchmarks.

  6. Hugging Face Blog58

    How Specializing the Vocabulary of SLMs Can Cut Inference Cost by 77% in Real Applications

    Dharma-AI describes VocabX, a method that rebuilds a small model's tokenizer around the cost of each token rather than its frequency, combining sub-word merges with multi-word "macro" tokens under a shared cost criterion. In a production Portuguese essay grader built on Qwen3-4B, the approach cut average response length from 350 to 101 tokens and measured inference cost by 77.0% with the same base model and training recipe, while the bench score rose from 0.703 to 0.810. The article previews results from a VocabX paper still under review, noting the numbers may change before publication.

  7. Hugging Face Blog27

    Profiling TorchNeuron (Part 1): System-level profiling on AWS Trainium.

    This post walks through system-level profiling of inference on AWS Trainium using TorchNeuron, its open source native PyTorch backend, starting from scratch with no prior profiling experience. It shows how to configure torch.profiler with NeuronConfig and NeuronProfiler to capture a clean trace, read the key_averages() table to find operations consuming Neuron device time, and follow an operation through Neuron Explorer from framework dispatch to hardware execution. Using three RMSNorm implementations, it reports naive ops at 2,355 µs of Neuron time versus 403 µs for torch.rms_norm and 376 µs for naive plus torch.compile.

Oct 21Wed
  1. Mistral AI58

    Mistral Advances AI for Engineering with NVIDIA

    Mistral and NVIDIA are collaborating to integrate Mistral Large 4 and Mistral's data generation and physics modeling pipelines with NVIDIA CUDA-X libraries, Warp and Omniverse to accelerate engineering simulation and enable closed-loop workflows connecting simulation, AI model development and design. The partnership will develop public benchmarks and open datasets for evaluating physics models and agentic AI in CAE and EDA, assessing agents' tool use, multistep workflows, technical correctness, execution time and computational cost. The work builds on the companies' prior co-development of Mistral NeMo and their collaboration through the NVIDIA Nemotron Coalition.

  3. NVIDIA Blog60

    Bringing the Spark to Europe: Local Intelligence Powers Next Wave of Agents

    At GTC Berlin, NVIDIA showcased its local AI hardware lineup: RTX Spark for personal agents on Windows PCs, DGX Spark for Linux developers, and DGX Station for enterprise workloads, with a Windows version due in December. Announcements included Underdog Desktop Agent for RTX Spark, DGX Spark 64GB systems rolling out Oct. 23, support for clusters of up to eight DGX Sparks, NVIDIA Sync Model Launcher, and the new Recursive Flint local coding model. NVIDIA also added day-zero support for new Nemotron, GLM, DeepSeek, Qwen, MiniMax and FLUX.3 models, plus accelerations for llama.cpp and vLLM.

    Why it matters: It offers a detailed roundup of NVIDIA's local AI hardware and software announcements for developers and enterprises.

  4. NVIDIA Blog72

    Google Cloud and NVIDIA Announce General Availability of NVIDIA Vera Rubin Infrastructure

    Google Cloud and NVIDIA announced general availability of NVIDIA Vera Rubin-based A5X instances next month, with planned expansion to Europe, combining Vera Rubin NVL72, ConnectX-9 SuperNICs, Google Cloud Virgo Network and fourth-generation liquid cooling for training, inference and large-scale reinforcement learning. Google Cloud plans to scale the infrastructure to as many as 960,000 Rubin GPUs across multiple sites, and Ineffable Intelligence, founded by AlphaGo creator David Silver, was the first customer to access A5X. Google Cloud also said it will be among the first cloud providers to offer NVIDIA Confidential Computing at rack scale on bare metal instances such as A4X Max powered by GB300 NVL72, and is making Confidential Computing generally available on G4 VMs powered by RTX PRO 6000 Blackwell Server Edition GPUs.

    Why it matters: The announcement details Google Cloud's Vera Rubin rollout plans, early customer use cases and confidential computing offerings, useful for readers tracking AI infrastructure availability.

  5. NVIDIA Blog58

    NVIDIA Cloud Partners Scale Across Europe — Sovereign, Open and Built for Safe AI Agents

    NVIDIA Cloud Partners across Europe, the Nordics, Africa and Central Asia are expanding regional compute capacity for model training and inference, with Nscale reporting that NVIDIA DSX MaxLPS enabled 37% more GPUs and nearly 50% higher aggregate inference throughput within the same power budget on GB300 NVL72 systems. Partners including Nebius, Baseten, Fireworks, Mistral and Verda are expanding open-model training and inference services, while the NVIDIA OpenShell Reference Architecture, supported by partners such as Aleria, Baseten, Cassava, CoreWeave, Crusoe and Nebius, is designed to give organizations full-stack control over what AI agents can access and do.

  6. NVIDIA Blog50

    Europe’s Industrial Leaders Reinvent Engineering and Manufacturing With NVIDIA

    NVIDIA announced that European industrial companies are using its CUDA-X and Omniverse libraries and Cosmos and Nemotron open models across engineering and manufacturing, and that the Factory Operations Blueprint (FOX) is now in early access on GitHub for building factory manager and specialist agents. Siemens is integrating FOX-built agents into Intelligence Center X for an estimated 20% cost savings and used Cosmos Data Factory tools at its Erlangen electronics factory to cut inspection calibration time by 40%. The announcement also cites Mercedes-Benz, Audi, Volkswagen Sachsen, Schaeffler, imec, Infineon, Leonardo and others applying NVIDIA simulation and digital twin technologies.