All news with #nvidia tag

🔒 On day one of Pwn2Own Berlin 2026 researchers earned $523,000 exploiting 24 unique zero-days, led by Orange Tsai, who collected $175,000 after chaining four logic flaws to escape the Microsoft Edge sandbox. Windows 11 was rooted three times for new privilege-escalation bugs, and Valentina Palmiotti secured payouts for Red Hat Workstations and an NVIDIA Container Toolkit flaw. The event focuses on enterprise and AI-targeted technologies.

🚀 Imgix serves over 8 billion images and videos daily and has migrated its real-time processing stack to G4 VMs on Google Cloud, powered by NVIDIA RTX PRO 6000 Blackwell GPUs. The move delivered a 50% reduction in median latency and a 5–6× increase in throughput per node without rewriting core application code. Imgix combines nvJPEG, NVENC/NVDEC, custom Vulkan compute shaders and CUDA libraries to accelerate decoding, transformation and encoding, while autoscaling, self-healing GPU management and a 2.5PB GCS cache enable fast, reliable global delivery.

🚀 Amazon now offers P5.48xl EC2 instances in SageMaker Studio notebooks across US West (San Francisco), Asia Pacific (Tokyo, Mumbai, Sydney, Jakarta), and Europe (London, Stockholm). These instances are powered by NVIDIA H100 Tensor Core GPUs and deliver up to 4x performance improvements and up to 40% lower training cost versus prior GPU generations. They are suited for training and serving complex LLMs, diffusion models, and other generative AI and HPC workloads. See the developer guides for setup with JupyterLab and CodeEditor and consult regional pricing for details.

🚀 Amazon announces general availability of EC2 P6-B200 instances in AWS US East (N. Virginia) for use with SageMaker Studio notebooks. These instances feature eight NVIDIA Blackwell GPUs, 1440 GB of high-bandwidth GPU memory, and 5th Generation Intel Xeon (Emerald Rapids) processors, offering up to 2x training performance vs P5en. They enable interactive development and fine-tuning of large foundation models directly in JupyterLab or CodeEditor for generative AI workloads.

🚀 Amazon Web Services has announced general availability of Amazon EC2 P5.4xl instances for SageMaker Studio notebooks, powered by NVIDIA H100 Tensor Core GPUs. These instances offer up to 4x faster time-to-solution versus previous-generation GPU instances and claim up to 40% lower training cost for ML models. They are designed to accelerate training and deployment of demanding DL and HPC workloads, including large language models and diffusion models. P5.4xl is available now in select US, Asia Pacific, and South America regions, with developer guides and pricing details provided.

🚀 Amazon Web Services announced general availability of Amazon EC2 G6 instances for SageMaker Studio notebooks in the Middle East (Dubai) and Asia Pacific (Malaysia). G6 instances pair up to eight NVIDIA L4 Tensor Core GPUs (24 GB each) with third-generation AMD EPYC processors, delivering roughly 2× better deep-learning inference performance than G4dn. These instances support interactive model deployment and training for generative AI fine-tuning, NLP, vision, and recommender workloads. Refer to developer guides for JupyterLab and CodeEditor setup and the pricing page for cost details.

🚀 Amazon Web Services announced general availability of EC2 G6e instances on SageMaker Studio notebooks in Dubai, Tokyo, Seoul, Frankfurt, Stockholm and Spain. G6e instances provide up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB per GPU and 3rd‑generation AMD EPYC processors, delivering up to 2.5× performance versus G5. They target interactive model testing, training and generative AI fine‑tuning, and can host LLMs up to 13B parameters as well as diffusion models for image, video and audio generation. Developer guides cover JupyterLab and CodeEditor setup; pricing is available on the AWS pricing page.

🚀 Amazon Web Services has announced the general availability of EC2 P4de instances on SageMaker Studio notebooks in Asia Pacific (Tokyo, Singapore) and Europe (Frankfurt). Each P4de packs eight NVIDIA A100 GPUs with 80GB HBM2e (640GB total), offering 2× the per‑GPU memory versus P4d. AWS reports up to 60% faster ML training and roughly 20% lower training cost compared to P4d, benefiting large high‑resolution datasets and reducing model training time. Developers can follow the SageMaker JupyterLab and CodeEditor guides and consult pricing for cost planning.

🔒 NVIDIA confirmed that GeForce NOW user information was exposed in a breach limited to Armenia after a regional partner's infrastructure was compromised. The company said its own network and NVIDIA-operated services were not affected and it is assisting the partner. Regional operator GFN.am said the incident occurred March 20–26 and that impacted users will be notified. Exposed fields reportedly include names, emails, phone numbers, dates of birth and usernames; no passwords were exposed.

🚀 Amazon Web Services now offers Amazon EC2 G6 instances powered by NVIDIA L4 GPUs in the AWS European Sovereign Cloud (Germany). These instances address graphics-intensive and machine learning workloads — including natural language processing, translation, video and image analysis, speech recognition, and personalization — with up to 8 L4 Tensor Core GPUs (24 GB each), third-generation AMD EPYC processors, up to 192 vCPUs, 100 Gbps networking, and 7.52 TB local NVMe SSD. G6 instances are available as On-Demand, Spot, and Savings Plans and can be launched via the AWS Management Console, CLI, or SDKs. They expand AWS's GPU capabilities for customers with sovereignty and compliance needs.

🚀 Amazon EC2 G7e instances with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in the Europe (London) region. G7e delivers up to 2.3x inference performance over G6e and supports up to eight GPUs with 96 GB each, 5th Gen Intel Xeon processors, 192 vCPUs, and up to 1600 Gbps networking. G7e includes NVIDIA GPUDirect P2P and GPUDirect RDMA with EFA for lower-latency multi-GPU and multi-node workloads, and is available as On‑Demand, Spot, and via Savings Plans.

🚀 Amazon EC2 P6-B200 instances with NVIDIA Blackwell GPUs are now available in AWS GovCloud (US-West) region as of May 6, 2026. They offer up to 2x performance versus P5en for AI training and inference and include eight Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and a 60% increase in memory bandwidth. P6-B200 runs on 5th Generation Intel Xeon processors (Emerald Rapids), supports up to 3.2 terabits per second of EFAv4 networking, and is delivered in the p6-b200.48xlarge size.

🚀 Amazon Web Services announced that Amazon EC2 P6-B300 instances are now available in the US East (N. Virginia) Region. The p6-b300.48xlarge ships with 8x NVIDIA Blackwell Ultra GPUs, 2.1 TB high-bandwidth GPU memory, 6.4 Tbps EFA networking, 300 Gbps ENA throughput and 4 TB system memory. Compared with P6-B200, P6-B300 delivers 2x networking bandwidth and 1.5x GPU memory and TFLOPS (FP4, without sparsity), making it suited for training and serving large trillion-parameter foundation models and LLMs with improved token throughput and faster distributed training.

⚠️ Two independent research teams disclosed new Rowhammer-style attacks against NVIDIA Ampere GPUs that induce GDDR bitflips to gain arbitrary read/write access to host memory, enabling full system compromise when IOMMU is disabled by default in many BIOS settings. The proofs of concept — GDDRHammer and GeForge — manipulate GPU page tables and page directories to escalate privileges and, in demonstrations, open root shells on affected machines. A subsequent variant was shown to succeed even with IOMMU enabled; tested cards include RTX 3060, RTX A6000, and RTX 6000.

🖥️ Amazon ECS Managed Instances now exposes NVIDIA GPU metrics through CloudWatch Container Insights with enhanced observability. Customers can monitor GPU capacity, utilization, memory usage, device-level hardware health, and thermal conditions for containerized workloads. The metrics are available in all commercial AWS Regions; to use them, enable Container Insights with enhanced observability and launch GPU-accelerated EC2 instance types via an ECS Managed Instances capacity provider.

🚀 Amazon Bedrock now includes OpenAI GPT OSS (120B and 20B) and NVIDIA Nemotron models (Nano 9B v2, Nano 12B v2, Nano 30B, Super 120B), enabling developers to access open-weight foundation models through a single API. The integration is powered by Mantle, a distributed inference engine that provides serverless, high-performance inference, unified capacity pools, automated quota management, and OpenAI API compatibility. These models are available on AWS GovCloud (US) for compliant, enterprise-grade deployments.

🚀 Amazon SageMaker HyperPod now supports G7e and r5d.16xlarge instances to improve large-model development, training, and deployment at scale. G7e uses NVIDIA RTX PRO 6000 Blackwell GPUs, offering up to 2.3x better inference performance than G6e and up to 768 GB GPU memory for larger LLMs, multimodal, and agentic AI workloads. The r5d.16xlarge provides 64 vCPUs, 512 GB RAM, and NVMe storage for distributed preprocessing, feature engineering, and memory-heavy orchestration; G7e is available in select US and Asia Pacific regions while r5d.16xlarge is available across all HyperPod regions.

🔧 Amazon Elastic Container Service now includes NVIDIA GPU health monitoring and auto repair for ECS Managed Instances. The capability leverages NVIDIA Data Center GPU Manager (DCGM) to detect critical GPU hardware failures and proactively replace impaired instances to maintain availability for GPU-accelerated container workloads. You can view GPU health via the DescribeContainerInstances API and receive notifications through Amazon EventBridge. Auto repair is enabled by default on supported instances at no additional cost and is available in all AWS Commercial Regions.

🚀 Google Public Sector is launching coordinated initiatives to help partners build, certify, and bring AI solutions to government customers faster. The program includes a federal startup accelerator in collaboration with NVIDIA for AI-focused ISVs, an expanded ISV ATO Accelerator offering up to $1M in funding, and a new Distributor Channel Private Offer with Carahsoft. These efforts target procurement, compliance, and legacy environment barriers to speed deployment of mission-critical AI.

🚀 AWS has made Amazon EC2 G7e instances generally available in the Los Angeles Local Zone (us-west-2-lax-1b), bringing high-performance GPU compute closer to end users. G7e instances combine NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs with 5th-generation Intel Xeon Scalable (Emerald Rapids) processors to support creative workflows such as studio workstations, VFX editing, color correction, and enhanced real-time rendering. They are also targeted at AI workloads including LLM deployment, inference, and agentic AI at the edge; availability is via On Demand and Savings Plans and you can enable the Local Zone through AWS Global View or launch instances from the EC2 console, AWS CLI, and SDKs.