All news with #nvidia tag

🔒 Three widely used open-source AI/ML Python libraries — NVIDIA NeMo, Salesforce uni2TS, and Apple ml-flextok — were found vulnerable to remote code execution when model metadata was treated as executable configuration. The root cause is unsafe use of configuration-driven instantiation (for example Hydra's instantiate()) that accepts attacker-controlled _target_ values. Vendors released patches and CVE notices; users should apply fixes, restrict allowed targets, and avoid loading models from untrusted sources.

🔒 Palo Alto Networks announced that Prisma AIRS, accelerated on the NVIDIA BlueField DPU, is now part of the NVIDIA Enterprise AI Factory validated design. The integration embeds zero trust runtime security into AI infrastructure by running Prisma AIRS Network Intercept on BlueField and extending enforcement to cloud environments. It leverages NVIDIA DOCA and DOCA Argus telemetry to feed Cortex XSIAM and Cortex XSOAR for AI-driven detection and response, and recommends hyperscale firewall clusters for defense-in-depth and improved TCO.

🚀 Azure says its long-range datacenter strategy already accommodates NVIDIA Vera Rubin NVL72 racks, enabling rapid, large-scale rollouts across current Fairwater sites and planned AI superfactories. Microsoft highlights prior experience with Ampere, Hopper, GB200 and GB300 generations and claims its power, cooling, networking, and memory upgrades align with Rubin’s NVLink, ConnectX‑9, and HBM4 requirements. The post frames co-design work as reducing deployment risk and accelerating customer access to higher-performance inference and training at scale.

🔒 Check Point and NVIDIA announced an integrated security capability to protect AI "factories" across the entire AI lifecycle, from data ingestion and model training to deployment and inference. The effort targets growing risks such as prompt manipulation and attacks on GenAI infrastructure, which Gartner and other industry surveys identify as rising threats. The collaboration focuses on unified visibility, real-time detection, runtime protection, and centralized policy enforcement to reduce operational risk and help organizations meet compliance and governance requirements.

🚀 Amazon Web Services has launched Amazon EC2 G5 instances powered by NVIDIA A10G Tensor Core GPUs in the Asia Pacific (Hong Kong) region to support graphics-intensive and machine learning workloads. These instances scale to eight A10G GPUs with 2nd-generation AMD EPYC processors, up to 192 vCPUs, 100 Gbps networking and 7.6 TB of NVMe local storage across eight size options. Customers can tune performance with NVIDIA drivers for compute, gaming, or workstation workloads and purchase capacity as On-Demand or Reserved Instances to meet cost and operational needs.

🔒 CrowdStrike and NVIDIA operationalized Nemotron LLMs to enable natural-language-to-CQL translation inside the Falcon platform. They leveraged millions of analyst queries, AST-based deduplication, and a PII scrubbing pipeline, then used NVIDIA NeMo Data Designer to generate synthetic natural-language descriptions for fine-tuning. Fine-tuning Llama-3.3-Nemotron-Super-49B-v1.5 with LoRA produced improved accuracy, interpretability through intermediate reasoning, and 96% valid-query accuracy versus frontier alternatives.

🚀 Amazon Bedrock now supports NVIDIA Nemotron 3 Nano 30B A3B, NVIDIA's efficient hybrid Mixture-of-Experts language model with a 256k token context window and native tool-calling support. The model delivers higher throughput for agentic, coding, and complex reasoning workloads while preserving the depth of larger models through advanced reinforcement learning and multi-environment post-training. Powered by Project Mantle, Bedrock provides serverless distributed inference, QoS controls, automated capacity management and OpenAI API compatibility across multiple AWS Regions.

🚀 Amazon Web Services has launched the compute-optimized EC2 C8a instances in the Europe (Spain) region. Powered by 5th Gen AMD EPYC processors running up to 4.5 GHz, C8a offers up to 30% higher performance and up to 19% better price-performance versus C7a, plus 33% greater memory bandwidth. Available in 12 sizes (including two bare-metal options), they target high-performance, latency-sensitive workloads and support Savings Plans, On-Demand, and Spot purchasing.

🚀 The NVIDIA Run:ai Model Streamer now supports native Google Cloud Storage access, accelerating model load and inference startup for vLLM workloads on GKE. By streaming tensors directly from Cloud Storage into GPU memory and using distributed, NVLink-aware transfers, the streamer dramatically reduces cold-start latency and idle GPU time. Enabling it in vLLM is a single-flag change and it can leverage GKE Workload Identity for secure, keyless access.

🔒 Microsoft is expanding Azure with on‑premises, edge, and hybrid options to deliver AI, resilience, and operational sovereignty. Azure Local provides integrated compute, storage, and networking on customer premises with GA features like Microsoft 365 Local and NVIDIA Blackwell GPUs, plus previews for disconnected operations and multi‑rack scale. Coupled with Azure IoT, Microsoft Fabric, and Azure Arc management enhancements, the updates enable near‑real‑time analytics, secure device identity, and a unified control plane for distributed estates. The goal is to accelerate AI and analytics while preserving data residency, continuity, and compliance for regulated or mission‑critical environments.

🚀 AWS has announced general availability of Amazon EC2 P6e-GB300 UltraServers powered by the NVIDIA GB300 NVL72. The new UltraServers deliver 1.5× GPU memory and 1.5× FP4 compute (without sparsity) compared with P6e-GB200, enabling higher-context inference and improved throughput for large models. Ideal for reasoning, Agentic AI, and production inference; contact your AWS sales representative to get started.

🚀 Amazon SageMaker HyperPod now supports NVIDIA Multi-Instance GPU (MIG), enabling administrators to partition a single GPU into multiple isolated devices to run simultaneous small generative AI tasks. Administrators can use an easy console configuration or a custom setup for fine-grained hardware isolation, allocate compute quotas across teams, and monitor real-time performance per partition via a utilization dashboard. Available on HyperPod clusters using the EKS orchestrator in multiple AWS Regions, this capability reduces wait times by letting data scientists run lightweight inference and interactive notebooks in parallel without consuming full GPU capacity.

🔧 Nvidia released the GeForce Hotfix Display Driver 581.94 to address gaming performance regressions reported after the October 2025 Windows update (KB5066835 [5561605]) affecting Windows 11 24H2 and 25H2 systems. The company notes this is a beta hotfix with an abbreviated QA cycle and is provided as-is to deliver targeted fixes more quickly. The driver is available from Nvidia Customer Care for Windows 10 x64 and Windows 11 x64 PCs.

🚀 Amazon Web Services has announced general availability of Amazon EC2 P6-B300 instances powered by NVIDIA Blackwell Ultra B300 GPUs. The p6-b300.48xlarge delivers eight GPUs, 2.1 TB of high-bandwidth GPU memory, 6.4 Tbps EFA networking, 300 Gbps ENA throughput, and 4 TB of system memory. It targets training and deploying trillion-parameter foundation models and LLMs, offering higher memory, compute, and networking versus P6-B200.

⚠️ Oligo Security researcher Avi Lumelsky disclosed a widespread insecure-deserialization pattern dubbed ShadowMQ that affects major AI inference engines including vLLM, NVIDIA TensorRT-LLM, Microsoft Sarathi-Serve, Modular Max Server and SGLang. The root cause is using ZeroMQ's recv_pyobj() to deserialize network input with Python's pickle, permitting remote arbitrary code execution. Patches vary: some projects fixed the issue, others remain partially addressed or unpatched, and mitigations include applying updates, removing exposed ZMQ sockets, and auditing code for unsafe deserialization.

🔒 Cybersecurity researchers disclosed a chain of remote code execution (RCE) vulnerabilities affecting AI inference frameworks from Meta, NVIDIA, Microsoft and open-source projects such as vLLM and SGLang. The flaws stem from reused code that called ZeroMQ’s recv-pyobj() and passed data directly into Python’s pickle.loads(), enabling unauthenticated RCE over exposed sockets. Vendors have released patches replacing unsafe pickle usage with JSON-based serialization and adding authentication and transport protections. Operators are urged to upgrade to patched releases and harden ZMQ channels, restrict network exposure, and avoid deserializing untrusted data.

🚀 Amazon Web Services has expanded availability of EC2 G6f instances powered by NVIDIA L4 GPUs to Europe (Spain) and Asia Pacific (Seoul), improving access for graphics and visualization workloads. G6f instances support GPU partitions as small as one-eighth of a GPU with 3 GB of GPU memory, enabling finer-grained right-sizing and cost savings compared to single‑GPU options. Instances are offered in multiple sizes paired with third‑generation AMD EPYC processors, and are purchasable as On‑Demand, Spot, or via Savings Plans; customers should use NVIDIA GRID driver 18.4 or later to launch these instances.

🚀 Microsoft announced the new Fairwater Azure AI datacenter in Atlanta, Georgia, expanding its planet-scale AI superfactory. The purpose-built facility integrates massive NVIDIA Blackwell GPU clusters on a single flat network and uses rack-level direct liquid cooling plus a two-story layout to maximize compute density and reduce latency. It also connects via a dedicated AI WAN to enable cross-site fungibility and dynamic workload allocation.

🔒 Check Point has made AI Cloud Protect powered by NVIDIA BlueField available for enterprise deployment, offering DPU-accelerated security for cloud AI workloads. The solution aims to inspect and protect GenAI traffic and prompts to reduce data exposure risks while integrating with existing cloud environments. It targets prompt manipulation and infrastructure attacks at scale and is positioned for organizations building AI factories.

🚀 Google Cloud is previewing managed DRANET on GKE, enabling Kubernetes to treat high-performance RDMA network interfaces as schedulable resources. The integration aligns NICs and GPUs by NUMA topology to reduce latency and increase throughput, while abstracting away operational complexity. It launches with the new A4X Max instances to deliver topology-aware networking for large multi-GPU AI workloads. Developers can request specific network interfaces in pod specs and rely on GKE to co-schedule NICs and accelerators, improving utilization and simplifying operations.