All news with #nvidia tag

🚀 Google Cloud is expanding its NVIDIA collaboration with the new A4X Max instances powered by NVIDIA GB300 NVL72, delivering 72 GPUs with high‑bandwidth NVLink and shared memory for demanding multimodal reasoning. GKE now supports DRANET for topology‑aware RDMA scheduling and integrates NVIDIA NeMo Guardrails into GKE Inference Gateway, while Vertex AI Model Garden will host NVIDIA Nemotron models. Vertex AI Training adds NeMo and NeMo‑RL recipes and a managed Slurm environment to accelerate large‑scale training and deployment.

🚀 Microsoft and NVIDIA announced expanded AI infrastructure on Azure, bringing NVIDIA RTX PRO 6000 Blackwell Server Edition to Azure Local, new Nemotron and Cosmos models via Azure AI Foundry, and broader support for Run:ai and GB300 NVL72 supercomputing clusters. These updates enable on-premises and edge AI with cloud-like management, improved GPU utilization, and infrastructure tailored for frontier reasoning, multimodal workloads, and real-time inferencing. Microsoft also highlighted NVIDIA Dynamo optimizations for ND GB200-v6 VMs to boost inference throughput at scale.

🔒 Palo Alto Networks outlines a platform-centric approach to protect the enterprise AI Factory, announcing integration of Prisma AIRS with NVIDIA BlueField DPUs. The collaboration embeds distributed zero-trust security directly into infrastructure, delivering agentless, penalty-free runtime protection and real-time workload threat detection. Validated on NVIDIA RTX PRO Server and optimized for BlueField‑3, with BlueField‑4 forthcoming, the solution ties into Strata Cloud Manager and Cortex for end-to-end visibility and control, aiming to secure AI operations at scale without compromising performance.

🚀 The G4 VM is now generally available on Google Cloud, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and offering up to 768 GB of GDDR7 memory per instance class. It targets latency-sensitive and regulated workloads for generative AI, real-time rendering, simulation, and virtual workstations. Features include FP4 precision support, Multi-Instance GPU (MIG) partitioning, an enhanced PCIe P2P interconnect for faster multi‑GPU All-Reduce, and an NVIDIA Omniverse VMI on Marketplace for industrial digital twins.

🚀 Google Cloud's newly GA G4 VMs combine NVIDIA RTX PRO 6000 Blackwell GPUs with a custom, software-defined PCIe fabric to enable high-performance peer-to-peer (P2P) GPU communication. The platform accelerates collective operations like All-Gather and All-Reduce without code changes, delivering up to 2.2x faster collectives. For tensor-parallel inference, customers can see up to 168% higher throughput and up to 41% lower inter-token latency. G4 integrates with GKE Inference Gateway for horizontal scaling and production deployments.

🤝 At the end of October in Washington, D.C., Google Cloud and NVIDIA will lead a week of events highlighting advances in AI, high-performance computing, and secure mission deployments. NVIDIA GTC DC (Oct. 27–29) features keynotes, demos, and hands-on sessions showcasing next-generation models and infrastructure. The Google Public Sector Summit (Oct. 29) convenes government leaders to explore practical uses of technologies like Gemini for Government and discuss secure, scalable AI adoption for mission impact.

🔧 Microsoft details OCP contributions to accelerate open-source infrastructure for frontier-scale AI, focusing on power, cooling, networking, security, and sustainability. It highlights innovations such as solid-state transformers, a power-stabilization paper with OpenAI and NVIDIA, and a next-generation HXU for liquid cooling. Networking efforts include ESUN and scale-up Ethernet workstreams, while security contributions introduce Caliptra 2.1, Adams Bridge 2.0, and L.O.C.K. The post also advances fleet lifecycle management, carbon accounting, and waste-heat reuse for globally deployable AI datacenters.

🚀 Microsoft Azure announced the first production-scale cluster using more than 4,600 NVIDIA GB300 NVL72 (Blackwell Ultra) GPUs, co-engineered with NVIDIA to support OpenAI and other frontier AI workloads. The new ND GB300 v6 VMs are optimized for reasoning models, agentic systems, and multimodal generative AI, delivered on rack-scale systems with 72 GPUs per rack and 36 NVIDIA Grace CPUs. Microsoft says this infrastructure will shorten training from months to weeks and will scale to hundreds of thousands of Blackwell Ultra GPUs globally.

⚠ Cisco Talos disclosed five vulnerabilities in NVIDIA's CUDA Toolkit components and one use-after-free flaw in Adobe Acrobat Reader. The Nvidia issues affect tools like cuobjdump (12.8.55) and nvdisasm (12.8.90), where specially crafted fatbin or ELF files can trigger out-of-bounds writes, heap overflows, and potential arbitrary code execution. The Adobe bug (2025.001.20531) involves malicious JavaScript in PDFs that can reuse freed objects, leading to memory corruption and possible remote code execution if a user opens a crafted document.

🔬 Researchers from Georgia Institute of Technology and Purdue University describe WireTap, a physical memory-bus interposer attack that passively inspects DDR4 traffic to recover secrets from Intel SGX enclaves. By exploiting deterministic memory encryption, the team built an oracle enabling a full key-recovery of an SGX ECDSA attestation key from the Quoting Enclave. The prototype uses inexpensive, off-the-shelf equipment (roughly $1,000) and can be introduced via supply-chain compromise or local physical access. Intel says the scenario requires physical access and falls outside its memory-encryption threat model.

🚀 Microsoft unveiled Fairwater, a purpose-built AI datacenter in Wisconsin and sister sites in Norway and the UK, designed to operate as a single, global-scale supercomputer. The facility deploys interconnected racks of NVIDIA GB200 servers (72 GPUs per rack) and claims 10× the performance of the world’s fastest supercomputer. It combines closed-loop liquid cooling, exabyte-scale storage and an AI WAN to enable distributed training and large-scale inference across Azure.

🔒 CrowdStrike describes how the Falcon platform delivers unified visibility and lifecycle defense across the full AI stack, from GPUs and training data to inference pipelines and SaaS agents. The post highlights integrations with NVIDIA, AWS, Intel, Dell, Meta, and Salesforce to extend protection into infrastructure, data, models, and applications. It also introduces agentic defense via Charlotte AI for autonomous triage and rapid response, and emphasizes governance controls to prevent data leaks and adversarial manipulation.

🚀 Amazon Web Services announced general availability of EC2 P6-B200 instances for SageMaker notebooks. These instances include eight NVIDIA Blackwell GPUs with 1,440 GB of high-bandwidth GPU memory and 5th Gen Intel Xeon processors, offering up to 2x the training performance versus P5en. They enable interactive development and fine-tuning of large foundation models in JupyterLab and CodeEditor, and are available in US East (Ohio) and US West (Oregon).

⚡ This post announces a reproducible recipe to deploy NVIDIA Dynamo for disaggregated LLM inference on Google Cloud’s AI Hypercomputer using Google Kubernetes Engine, vLLM, and A3 Ultra (H200) GPUs. The recipe separates prefill and decode phases across dedicated GPU pools to reduce contention and lower latency. It includes single-node and multi-node examples and step-by-step deployment actions. The repository provides configuration guidance and future plans for broader GPU and engine support.

🔒 Investment in AI data centers is accelerating globally, creating not only rising energy demand and emissions but also an expanded surface of cyber threats. AI facilities rely on GPUs, ASICs and FPGAs, which introduce side-channel, memory-level and GPU-resident malware risks that differ from traditional CPU-focused threats. Organizations should require operators to implement supply-chain vetting, physical shielding (for example, Faraday cages), continuous model auditing and stronger personnel controls to reduce model exfiltration, poisoning and foreign infiltration.

🚀 Baseten reports major cost-performance gains for AI inference by combining Google Cloud A4 VMs powered by NVIDIA Blackwell GPUs with Google Cloud’s Dynamic Workload Scheduler. The company cites 225% better cost-performance for high-throughput inference and 25% improvement for latency-sensitive workloads. Baseten pairs cutting-edge hardware with an open, optimized software stack — including TensorRT-LLM, NVIDIA Dynamo, and vLLM — and multi-cloud resilience to deliver scalable, production-ready inference.

🚀 Amazon SageMaker Training and Processing Jobs now supports the new EC2 P5 instance size with a single NVIDIA H100 GPU, offering the P5.4xlarge configuration for cost‑effective ML and HPC workloads. The instance enables fine-grained scaling so customers can begin with smaller configurations and expand incrementally, improving cost management and infrastructure flexibility. P5.4xlarge is available via SageMaker Flexible Training Plans and in select regions through On‑Demand and Spot.

🔬 This blog highlights how Microsoft Azure and NVIDIA combine cloud infrastructure and GPU-accelerated AI tooling to speed scientific discovery and commercial deployment. It profiles three startups—Pangaea Data, Basecamp Research, and Global Objects—demonstrating applications from clinical decision support to large-scale protein databases and photorealistic digital twins. The piece emphasizes measurable outcomes, compliance, and the importance of scalable compute and optimized AI frameworks for real-world impact.

🚀 Amazon has launched EC2 G6 instances powered by NVIDIA L4 GPUs in the Middle East (UAE) Region, expanding cloud GPU capacity for graphics and ML workloads. G6 instances offer up to 8 L4 GPUs with 24 GB per GPU, third-generation AMD EPYC processors, up to 192 vCPUs, 100 Gbps networking, and up to 7.52 TB local NVMe storage. They are available via On-Demand, Reserved, Spot, and Savings Plans and can be managed through the AWS Console, CLI, and SDKs.