# Data Center / Cloud Published articles for Data Center / Cloud. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T17:00:11Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Nemotron](), [Routing (disambiguation)]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [architecture](), [cost](), [data-center-cloud](), [deployment](), [llms](), [memory](), [mixture-of-experts-moe](), [models](), [moe](), [nemo](), [nemotron](), [performance](), [router](), [routing](), [tokens]() ### AI overview This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model. ### Source excerpt How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the... ## How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories DevFeed: [How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [Nvidia](), [AI Factory](), [AI Infrastructure](), [Hardware]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [hardware](), [industry](), [networking](), [networking-communications](), [nvidia](), [nvlink](), [resiliency](), [vera-rubin]() ### AI overview The article describes how NVIDIA NVLink 6 supports resiliency in large-scale AI factories. It explains that Vera Rubin NVL72 connects 72 Rubin GPUs into a single scale-up domain and outlines a multilayer approach using lossless networking, error correction, retry, flow control, and error containment to support continuous training and inference operations. ### Source excerpt For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-02T16:04:19Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Large Language Model]() Tags: [accelerate](), [agentic-ai-generative-ai](), [ai](), [ai-inference](), [ai-models](), [batch](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [inference](), [inference-performance](), [llm](), [llms](), [nvfp4](), [training-ai-models]() ### AI overview The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula. ### Source excerpt This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and... ## How to Size GPUs for AI Inference and TCO Without Overspending DevFeed: [How to Size GPUs for AI Inference and TCO Without Overspending]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-01T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Inference](), [GPU](), [Inference Performance](), [Large Language Model](), [cloud-infrastructure](), [Optimization]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [concurrency](), [cost](), [data-center-cloud](), [gpu](), [inference-performance](), [latency](), [llm](), [mlops](), [quantization]() ### AI overview A practical guide to sizing GPU infrastructure for AI inference workloads while balancing latency, concurrency, model choice, deployment strategy, and total cost of ownership. ### Source excerpt The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently... ## Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect DevFeed: [Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-28T17:06:28Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [C++](), [Orchestration]() Tags: [ai](), [ai-inference](), [ai-models](), [api](), [applications](), [c-plus-plus](), [data-center-cloud](), [deployment](), [developer-tools-techniques](), [edge-computing](), [hugging-face](), [inference](), [integration](), [kernel](), [model](), [nvidia](), [open](), [open-source](), [orchestration](), [python](), [pytorch](), [tensorrt]() ### AI overview The article explains NVIDIA TensorRT Model Connect, a collection of modifiable reference implementations for deploying supported open models from a Hugging Face ID or local checkpoint to native C++ inference. It describes a two-phase deployment bundle workflow, semantic and module-level C++ APIs, and custom GPU-kernel integration. ### Source excerpt Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,... ## NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure DevFeed: [NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure]() Original publisher: [Read original article]() Author: Farshad Ghodsian Published: 2026-08-26T21:06:58Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [AI Infrastructure](), [Nvidia](), [AI Platform](), [datacenter](), [Inference](), [systems]() Tags: [accelerate](), [agentic-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [architecture](), [compute](), [data-center](), [data-center-cloud](), [deployment](), [inference](), [infrastructure](), [integration](), [memory](), [model](), [models](), [networking-communications](), [nvidia](), [nvlink](), [performance](), [platform](), [scale](), [support](), [top-stories]() ### AI overview NVIDIA NVLink Fusion connects custom XPUs and CPUs to NVIDIA's AI infrastructure platform, while NVHBM provides validated HBM base-die technology intended to increase memory bandwidth, save package area, and reduce power consumption. The article describes benefits for training and large-scale inference, including up to 30% more memory bandwidth per stack than standard HBM4e. ### Source excerpt AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads,... ## Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules DevFeed: [Giga-Scale AI and the Ethernet Evolution: How Spectrum-X Ethernet Rewrites the Rules]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-24T15:08:39Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Spectrum-X](), [Ethernet](), [Generative AI](), [networking](), [datacenter](), [Nvidia]() Tags: [ai](), [ai-agent](), [ai-factory](), [ai-networking](), [data-center](), [data-center-cloud](), [ethernet](), [generative-ai](), [infrastructure](), [internet-communications](), [latency](), [low-latency](), [networking-communications](), [nvidia](), [resilience](), [spectrum-x]() ### AI overview This article explains how the growth of distributed generative AI training has made scale-out networking a major data center performance bottleneck. It contrasts traditional Ethernet with NVIDIA Spectrum-X Ethernet, a hardware-accelerated architecture that co-designs switches and host-side NICs to provide predictable low latency, high fabric utilization, and resilience for large AI workloads. It also introduces Spectrum-X Multiplane technology and describes how AI collective communication exposes limitations in conventional ECMP routing and congestion handling. ### Source excerpt The massive growth of generative AI has fundamentally altered data center design. As distributed model training scales to span hundreds of thousands of GPUs,... ## NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt DevFeed: [NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-24T15:00:05Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI-assisted coding]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [agentic-coding](), [ai-agent](), [ai-agents](), [ai-factory](), [ai-infrastructure](), [benchmark](), [blackwell](), [cache](), [cloud-networking](), [cloud-services](), [data-center-cloud](), [gb300-nvl72](), [hardware](), [inference](), [long-context](), [mixture-of-experts-moe](), [nemotron](), [nvidia](), [performance](), [software-defined-data-center](), [tools](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview The article examines the SemiAnalysis AgentX benchmark for measuring infrastructure efficiency on replayed agentic coding sessions. It compares Vera Rubin and Blackwell NVL72 systems by agentic throughput per megawatt and explains why dynamic, stateful sessions require more realistic evaluation than fixed-length inference tests. ### Source excerpt AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing... ## NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories DevFeed: [NVIDIA BlueField-4 Powers New Scale-In Network Infrastructure for Agentic AI Factories]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Factory](), [AI Infrastructure](), [Artificial Intelligence](), [networking](), [Nvidia](), [cloud-infrastructure](), [datacenter](), [Cloud](), [NVLink](), [InfiniBand]() Tags: [agentic-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [bluefield-dpu](), [connectx](), [data-center-cloud](), [data-centers](), [dsx](), [grace-cpu](), [infiniband](), [infrastructure](), [network](), [networking](), [networking-communications](), [nvidia](), [nvlink](), [top-stories](), [vera-cpu](), [vera-rubin]() ### AI overview NVIDIA describes Scale-In network infrastructure for agentic AI factories, centered on BlueField-4, DOCA, and Spectrum-X Ethernet. The architecture is intended to accelerate networking, storage, security, data movement, tenant isolation, and infrastructure operations as AI compute scales. ### Source excerpt Traditional cloud infrastructure was designed for predictable, general-purpose workloads and standard interfaces. Agentic AI factories connect diverse users,... ## How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [d-matrix](), [Vera Rubin](), [Inference Performance](), [long-context](), [Benchmark](), [Artificial Intelligence](), [gemma4](), [systems](), [Cache]() Tags: [agent](), [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-inference](), [benchmark](), [cache](), [collective](), [data-center-cloud](), [developer-tools-techniques](), [groq](), [groq-3-lpx](), [inference-performance](), [long-context](), [low-latency-inference](), [lpx](), [nvidia](), [nvidia-vera](), [performance](), [rubin-gpu](), [speed](), [systems](), [tokens](), [training-ai-models](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview NVIDIA Groq 3 LPX, paired with Vera Rubin NVL72, delivers high-interactivity AI inference for long-context workloads. A reported benchmark measured 3,431 output tokens per second on Gemma 4 31B with a 100K context. ### Source excerpt NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the... ## Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU DevFeed: [Solving Agentic AI Fleet Challenges with NVIDIA Vera CPU]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agent](), [agentic](), [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-factory](), [architecture](), [concurrency](), [cpu](), [data-center-cloud](), [latency](), [nvidia](), [nvidia-vera](), [orchestration](), [performance](), [reasoning](), [systems](), [telemetry](), [top-stories](), [vera-cpu](), [vera-rubin]() ### AI overview The article argues that AI-agent CPU fleets should use a balanced design that combines strong per-core performance for latency-bound sequential work with enough concurrency for intermittent parallel bursts. It presents NVIDIA Vera CPU as designed for those agentic workload patterns. ### Source excerpt AI factories are interconnected systems where fleet economics depend on how efficiently the entire stack converts power and capital into completed agent tasks.... ## Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS DevFeed: [Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Factory](), [Artificial Intelligence](), [Optimization](), [datacenter](), [GPU](), [Inference](), [Nvidia](), [Provisioning](), [systems]() Tags: [ai](), [ai-factory](), [ai-inference](), [compute](), [data-center](), [data-center-cloud](), [dsx](), [efficiency](), [gpu](), [inference](), [infrastructure](), [nvidia](), [nvidia-dsx](), [optimization](), [performance](), [scale](), [tokens](), [vera-rubin-nvl72]() ### AI overview NVIDIA DSX MaxLPS is presented as a suite of chip, thermal, system, and software technologies for increasing AI factory throughput within a fixed power budget. The article emphasizes application-level performance per watt, dynamic power allocation, software power optimization, and warm-water liquid cooling as ways to convert more site power into AI inference output. ### Source excerpt AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available... ## How Generative Recommenders Are Redefining RecSys at Scale DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [Language models]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [embedding](), [featured](), [generative](), [gpu](), [inference](), [latency](), [llms](), [machine-learning](), [machine-learning-artificial-intelligence](), [recommenders-personalization](), [scale]() ### AI overview The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale. ### Source excerpt Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and... ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## How to Choose Full-Stack Observability for NVIDIA AI Factories DevFeed: [How to Choose Full-Stack Observability for NVIDIA AI Factories]() Original publisher: [Read original article]() Author: Jorge Cardoso Published: 2026-08-12T16:13:47Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [observability](), [AI Infrastructure](), [NVIDIA DGX](), [InfiniBand](), [telemetry](), [dashboards](), [NCCL](), [Monitoring]() Tags: [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [distributed-training](), [featured](), [infiniband](), [infrastructure](), [monitoring](), [nccl](), [networking](), [networking-communications](), [nvidia-dgx](), [observability](), [operations](), [orchestration](), [performance](), [storage](), [systems](), [telemetry]() ### AI overview A practical guide to choosing a full-stack observability strategy for NVIDIA AI infrastructure. It explains how to connect telemetry across compute, networking, storage, orchestration, and applications, using an InfiniBand gray-failure example to show how degraded hardware and NCCL collective-operation delays can reduce distributed-training throughput. ### Source excerpt AI infrastructure spans multiple layers, from compute and networking to storage, orchestration, and applications. When performance degrades, identifying the... ## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### AI overview Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data. ### Source excerpt Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... ## How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure DevFeed: [How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-03T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [AI Platform]() Tags: [ai](), [data-center-cloud](), [developer-tools-techniques](), [featured](), [gpu](), [how-to](), [infrastructure](), [kubernetes](), [open-source]() ### AI overview A tutorial for running isolated tenant Kubernetes clusters for multiple teams on shared GPU hardware, using KAI Scheduler and vCluster. ### Source excerpt Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared... ## NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage DevFeed: [NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-03T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Nvidia](), [Benchmark](), [cpu](), [Encryption](), [Inference Performance](), [Security](), [Resilience](), [systems]() Tags: [agentic](), [agentic-ai](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [ai-factory](), [ai-inference](), [benchmark](), [benchmarks](), [bluefield-dpu](), [cloud-apis](), [cloud-networking](), [cloud-services](), [compression](), [cpu](), [data-center-cloud](), [doca](), [encryption](), [featured](), [infrastructure](), [networking-communications](), [nvidia](), [nvidia-vera](), [performance](), [security](), [software-defined-data-center](), [storage](), [systems](), [top-stories](), [vera-cpu](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview NVIDIA presents benchmark results for the Vera BlueField-4 STX Storage Processor in AI-native storage workloads. The results describe faster encryption and decryption, recovery, integrity checking, compression and decompression, and multi-stage storage processing than an x86 CPU, with lower CPU and power overhead. ### Source excerpt Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,... ## Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference DevFeed: [Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-31T22:16:17Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [long-context](), [Artificial Intelligence](), [GPU](), [GPU optimization](), [Large Language Model](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [featured](), [gpu](), [inference](), [inference-performance](), [long-context](), [memory](), [model-architecture](), [nvidia](), [performance]() ### AI overview This article examines how co-designing dense attention with GPU execution can improve throughput and interactivity for long-context inference. It analyzes group size, head dimension, sequence length, and the different compute and memory behavior of prefill and decode, including the effects of speculative decoding and prefix caching. ### Source excerpt As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because... ## NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure DevFeed: [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-30T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [debugging](), [Processes]() Tags: [ai](), [ai-infrastructure](), [benchmarks](), [blackwell](), [cloud](), [cloud-services](), [cpu](), [data-center-cloud](), [debugging](), [developer-tools-techniques](), [dgx-cloud](), [diagnostics](), [distributed-training](), [featured](), [gb200](), [gb300-nvl72](), [grace-cpu](), [hardware](), [hopper](), [infiniband](), [infrastructure](), [kernel](), [linux](), [nvidia](), [nvl72](), [performance](), [training]() ### AI overview A troubleshooting guide for closing AI-training throughput gaps between NVIDIA reference architectures and partner clusters. It covers configuration and installation issues across memory management, CPU power and NUMA placement, NCCL queue-pair concurrency, and hardware setup. ### Source excerpt Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We... ## Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing DevFeed: [Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-27T00:45:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [CUDA](), [GPU](), [Optimization](), [Nvidia](), [Simulation and Design](), [Hardware]() Tags: [ai](), [computational-chemistry-materials-science](), [compute](), [cuda](), [cuda-x](), [data-center-cloud](), [engineering](), [featured](), [gpu](), [hardware](), [industrial-digitalization-digital-twin](), [manufacturing](), [nvidia](), [optimization](), [physics](), [production](), [simulation-modeling-design]() ### AI overview Applied Materials and NVIDIA are presented as combining materials engineering, semiconductor manufacturing, CUDA-X libraries, GPU-accelerated simulation, physics-based modeling, and AI-driven digital twins in an end-to-end digital development model. The approach spans atomic-scale materials discovery, process development, and factory optimization, with Ginestra used to connect material defects and properties to predicted device performance. ### Source excerpt As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have... ## ModelExpress: Distributing Model Artifacts at the Speed of Light DevFeed: [ModelExpress: Distributing Model Artifacts at the Speed of Light]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-24T16:45:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [model-deployment](), [GPU](), [P2P](), [Amazon S3](), [autoscaling](), [Caching](), [cloud-infrastructure]() Tags: [agentic-ai-generative-ai](), [ai-foundation-models](), [ai-inference](), [autoscaling](), [data-center-cloud](), [developer-tools-techniques](), [dynamo-triton](), [featured](), [gpu](), [inference](), [inference-performance](), [jit](), [llms](), [local](), [model](), [s3](), [scale](), [speed](), [streaming]() ### AI overview NVIDIA ModelExpress (MX) reduces model-serving startup time by locating compatible model weights at the fastest available source. It prioritizes direct GPU-to-GPU transfer over P2P RDMA from an existing serving replica, while using object or local storage for initial bootstrapping and fallback. The article also describes reusing JIT kernel caches and distributing reinforcement-learning weight updates. ### Source excerpt Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse, moving... [Next page]()