# Networking / Communications

Published articles for Networking / Communications.

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## How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories

DevFeed: [How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories](<https://devfeed.tech/articles/how-nvidia-nvlink-6-delivers-multi-layer-resiliency-for-ai-factories-26914.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-nvlink-6-delivers-multi-layer-resiliency-for-ai-factories/>)

Author: Elizabeth Goodman

Published: 2026-09-15T16:55:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [NVLink](<https://devfeed.tech/topics/nvlink.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [industry](<https://devfeed.tech/tags/industry.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [resiliency](<https://devfeed.tech/tags/resiliency.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### 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...

## NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure

DevFeed: [NVIDIA NVLink Fusion Brings NVHBM to Next-Generation AI Infrastructure](<https://devfeed.tech/articles/nvidia-nvlink-fusion-brings-nvhbm-to-next-generation-ai-infrastructure-6903.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nvlink-fusion-brings-nvhbm-to-next-generation-ai-infrastructure/>)

Author: Farshad Ghodsian

Published: 2026-08-26T21:06:58Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [NVLink](<https://devfeed.tech/topics/nvlink.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [integration](<https://devfeed.tech/tags/integration.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [scale](<https://devfeed.tech/tags/scale.md>), [support](<https://devfeed.tech/tags/support.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### 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](<https://devfeed.tech/articles/giga-scale-ai-and-the-ethernet-evolution-how-spectrum-x-ethernet-rewrites-the-rules-6830.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/giga-scale-ai-ethernet-evolution-spectrum-x-ethernet-rewrites-rules/>)

Author: Elizabeth Goodman

Published: 2026-08-24T15:08:39Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Spectrum-X](<https://devfeed.tech/topics/spectrum-x.md>), [Ethernet](<https://devfeed.tech/topics/ethernet.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [networking](<https://devfeed.tech/topics/networking.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-networking](<https://devfeed.tech/tags/ai-networking.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [ethernet](<https://devfeed.tech/tags/ethernet.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [internet-communications](<https://devfeed.tech/tags/internet-communications.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [spectrum-x](<https://devfeed.tech/tags/spectrum-x.md>)

### 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 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](<https://devfeed.tech/articles/nvidia-bluefield-4-powers-new-scale-in-network-infrastructure-for-agentic-ai-factories-6889.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-bluefield-4-powers-new-scale-in-network-infrastructure-for-agentic-ai-factories/>)

Author: Michelle Horton

Published: 2026-08-24T15:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [bluefield-dpu](<https://devfeed.tech/tags/bluefield-dpu.md>), [connectx](<https://devfeed.tech/tags/connectx.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### 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 to Choose Full-Stack Observability for NVIDIA AI Factories

DevFeed: [How to Choose Full-Stack Observability for NVIDIA AI Factories](<https://devfeed.tech/articles/how-to-choose-full-stack-observability-for-nvidia-ai-factories-6847.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-to-choose-full-stack-observability-for-nvidia-ai-factories/>)

Author: Jorge Cardoso

Published: 2026-08-12T16:13:47Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [observability](<https://devfeed.tech/topics/observability.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [featured](<https://devfeed.tech/tags/featured.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [observability](<https://devfeed.tech/tags/observability.md>), [operations](<https://devfeed.tech/tags/operations.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [storage](<https://devfeed.tech/tags/storage.md>), [systems](<https://devfeed.tech/tags/systems.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>)

### 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...

## 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](<https://devfeed.tech/articles/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage-6914.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/>)

Author: Elizabeth Goodman

Published: 2026-08-03T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Security](<https://devfeed.tech/topics/security.md>), [Resilience](<https://devfeed.tech/topics/resilience.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [bluefield-dpu](<https://devfeed.tech/tags/bluefield-dpu.md>), [cloud-apis](<https://devfeed.tech/tags/cloud-apis.md>), [cloud-networking](<https://devfeed.tech/tags/cloud-networking.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [compression](<https://devfeed.tech/tags/compression.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [doca](<https://devfeed.tech/tags/doca.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [featured](<https://devfeed.tech/tags/featured.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [security](<https://devfeed.tech/tags/security.md>), [software-defined-data-center](<https://devfeed.tech/tags/software-defined-data-center.md>), [storage](<https://devfeed.tech/tags/storage.md>), [systems](<https://devfeed.tech/tags/systems.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### 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,...

## Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField

DevFeed: [Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField](<https://devfeed.tech/articles/scaling-agentic-ai-factories-through-extreme-co-design-with-nvidia-bluefield-6937.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/scaling-agentic-ai-factories-through-extreme-co-design-with-nvidia-bluefield/>)

Author: Michelle Horton

Published: 2026-07-16T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Network](<https://devfeed.tech/topics/network.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Security](<https://devfeed.tech/topics/security.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [systems](<https://devfeed.tech/topics/systems.md>), [NVIDIA Research](<https://devfeed.tech/topics/nvidia-research.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [policy](<https://devfeed.tech/tags/policy.md>), [storage](<https://devfeed.tech/tags/storage.md>), [tool](<https://devfeed.tech/tags/tool.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

This article explains how agentic AI and long-context inference create demanding data-path requirements for AI factories. It describes NVIDIA BlueField-4, Vera BlueField-4 STX, and DOCA as infrastructure technologies that offload, accelerate, and isolate networking, storage, security, telemetry, and control-plane services while improving utilization, latency, isolation, cost per token, and energy efficiency.

### Source excerpt

Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage...

## A Practical Guide to GPU-Initiated Communication for Molecular Dynamics at Scale

DevFeed: [A Practical Guide to GPU-Initiated Communication for Molecular Dynamics at Scale](<https://devfeed.tech/articles/a-practical-guide-to-gpu-initiated-communication-for-molecular-dynamics-at-scale-6755.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/a-practical-guide-to-gpu-initiated-communication-for-molecular-dynamics-at-scale/>)

Author: Michelle Horton

Published: 2026-07-09T17:15:04Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [cpu](<https://devfeed.tech/topics/cpu.md>)

Tags: [communication](<https://devfeed.tech/tags/communication.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [gromacs](<https://devfeed.tech/tags/gromacs.md>), [guide](<https://devfeed.tech/tags/guide.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [performance](<https://devfeed.tech/tags/performance.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [scale](<https://devfeed.tech/tags/scale.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>)

### AI overview

A practical guide to improving GROMACS molecular-dynamics scaling by replacing CPU-orchestrated MPI handoffs with GPU-initiated communication through NVSHMEM.

### Source excerpt

Molecular dynamics (MD) simulations are among the most demanding workloads in computational science. Using them, researchers can observe atomic behavior in...

## Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial

DevFeed: [Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial](<https://devfeed.tech/articles/maximize-spectral-efficiency-with-ai-native-ran-and-nvidia-ai-aerial-6881.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/maximize-spectral-efficiency-with-ai-native-ran-and-nvidia-ai-aerial/>)

Author: Michelle Horton

Published: 2026-07-07T17:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [cpu](<https://devfeed.tech/topics/cpu.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-networking](<https://devfeed.tech/tags/ai-networking.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [networking-communications](<https://devfeed.tech/tags/networking-communications.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### AI overview

The article explains how GPU acceleration and an AI-native RAN architecture can improve spectral efficiency in Massive MIMO deployments. It describes using more complex Layer 1 and Layer 2 tracking, scheduling, channel-estimation, and beamforming algorithms to improve throughput and quality of service.

### Source excerpt

Spectrum is one of the most valuable assets in wireless communications. Over the last 30 years, telecom operators in the US have spent more than $240B to...