# Vera Rubin

Published articles for Vera Rubin.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin

DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin-26913.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

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: [Groq 3 LPX](<https://devfeed.tech/topics/groq-3-lpx.md>), [LPX](<https://devfeed.tech/topics/lpx.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [groq](<https://devfeed.tech/topics/groq.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [drive](<https://devfeed.tech/tags/drive.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [power-management](<https://devfeed.tech/tags/power-management.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis.

### Source excerpt

Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...

## 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...

## Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack

DevFeed: [Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack](<https://devfeed.tech/articles/palantir-and-nvidia-deploy-a-sovereign-nemotron-supply-chain-stack-starting-with-the-1-3-million-parts-in-every-vera-rubin-rack-12372.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/palantir-and-nvidia-deploy-a-sovereign-nemotron-supply-chain-stack-starting-with-the-1-3-million-parts-in-every-vera-rubin-rack>)

Author: Harold Fritts

Published: 2026-09-10T20:56:11Z

Content type: news

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [cuOpt](<https://devfeed.tech/topics/cuopt.md>), [Complex Systems](<https://devfeed.tech/topics/complex-systems.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [complex-systems](<https://devfeed.tech/tags/complex-systems.md>), [cuopt](<https://devfeed.tech/tags/cuopt.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [supply-chain](<https://devfeed.tech/tags/supply-chain.md>), [systems](<https://devfeed.tech/tags/systems.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

Palantir and NVIDIA have deployed a sovereign AI stack for supply chain operations, initially using NVIDIA's own Vera Rubin supply chain as the first customer. The system combines Nemotron open models with Palantir Foundry and AIP, NVIDIA NeMo Data Libraries, and cuOpt to support materials allocation, scenario planning, optimization, and risk detection while keeping final decisions with supply chain experts.

### Source excerpt

Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA's own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded The post Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack appeared first on StorageReview.com.

## From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry

DevFeed: [From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry](<https://devfeed.tech/articles/from-wafer-out-to-first-token-codifying-supply-chain-expertise-with-nemotron-and-palantir-foundry-6824.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/from-wafer-out-to-first-token-codifying-supply-chain-expertise-with-nemotron-and-palantir-foundry/>)

Author: Elizabeth Goodman

Published: 2026-09-10T09:00:00Z

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: [datacenter](<https://devfeed.tech/topics/datacenter.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Software](<https://devfeed.tech/topics/software.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cuopt](<https://devfeed.tech/tags/cuopt.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [llms](<https://devfeed.tech/tags/llms.md>), [manufacturing](<https://devfeed.tech/tags/manufacturing.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-blackwell](<https://devfeed.tech/tags/nvidia-blackwell.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [production](<https://devfeed.tech/tags/production.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [software](<https://devfeed.tech/tags/software.md>), [supply-chain](<https://devfeed.tech/tags/supply-chain.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

NVIDIA describes how it measures and reduces the time from wafer-out to first token across complex Grace Blackwell and Vera Rubin supply chains. The article focuses on time-to-rack, critical material allocation, real-time visibility, redundancy, reliability, and codifying human expertise.

### Source excerpt

NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two...

## With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents

DevFeed: [With Groq 3 LPX in Full Production, NVIDIA Extends Vera Rubin Inference for Agents](<https://devfeed.tech/articles/with-groq-3-lpx-in-full-production-nvidia-extends-vera-rubin-inference-for-agents-6963.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/vera-rubin-lpx-spectrum-x-nvlink-fusion/>)

Author: NVIDIA Writers

Published: 2026-08-24T15:00:41Z

Content type: news

Language: en

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

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>), [networking](<https://devfeed.tech/topics/networking.md>), [gemma4](<https://devfeed.tech/topics/gemma4.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bluefi](<https://devfeed.tech/tags/bluefi.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cpx](<https://devfeed.tech/tags/cpx.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-spectrum-x-ethernet](<https://devfeed.tech/tags/nvidia-spectrum-x-ethernet.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

NVIDIA announces Groq 3 LPX production availability alongside Vera Rubin NVL72, positioning the integrated compute, networking, and inference stack for long-context, agentic AI workloads. The article highlights a benchmark result of 3,400 output tokens per second on Gemma 4 31B and describes partner adoption of Vera Rubin, Spectrum-X Multiplane, and Groq 3 LPX.

### Source excerpt

The next era of AI inference won't be defined by a single breakthrough chip, network or system. It'll be defined by how every layer of the AI factory works together. That's why NVIDIA is extending Vera Rubin NVL72 with fast token generation for agentic systems. Announced today, the NVIDIA Vera Rubin rack-scale system NVIDIA Groq [...]

## 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](<https://devfeed.tech/articles/nvidia-vera-rubin-and-blackwell-set-a-new-standard-for-agentic-ai-performance-per-watt-6912.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-vera-rubin-and-blackwell-set-a-new-standard-for-agentic-ai-performance-per-watt/>)

Author: Elizabeth Goodman

Published: 2026-08-24T15:00:05Z

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-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cache](<https://devfeed.tech/tags/cache.md>), [cloud-networking](<https://devfeed.tech/tags/cloud-networking.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [software-defined-data-center](<https://devfeed.tech/tags/software-defined-data-center.md>), [tools](<https://devfeed.tech/tags/tools.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### 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](<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 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](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin-6843.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

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: [d-matrix](<https://devfeed.tech/topics/d-matrix.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [gemma4](<https://devfeed.tech/topics/gemma4.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Cache](<https://devfeed.tech/topics/cache.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [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-inference](<https://devfeed.tech/tags/ai-inference.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cache](<https://devfeed.tech/tags/cache.md>), [collective](<https://devfeed.tech/tags/collective.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [speed](<https://devfeed.tech/tags/speed.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### 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](<https://devfeed.tech/articles/solving-agentic-ai-fleet-challenges-with-nvidia-vera-cpu-6941.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/solving-agentic-ai-fleet-challenges-with-nvidia-vera-cpu/>)

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 Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [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>), [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>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [latency](<https://devfeed.tech/tags/latency.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [systems](<https://devfeed.tech/tags/systems.md>), [telemetry](<https://devfeed.tech/tags/telemetry.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

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....

## 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,...

## Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

DevFeed: [Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI](<https://devfeed.tech/articles/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai-6863.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/>)

Author: Eduardo Alvarez

Published: 2026-07-21T18:15:00Z

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>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [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>), [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>), [architecture](<https://devfeed.tech/tags/architecture.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cache](<https://devfeed.tech/tags/cache.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>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [scale](<https://devfeed.tech/tags/scale.md>), [tensor-cores](<https://devfeed.tech/tags/tensor-cores.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [training](<https://devfeed.tech/tags/training.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

This article examines the NVIDIA Rubin GPU architecture and its co-designed Vera Rubin platform for agentic AI inference. It describes how Tensor Cores, HBM4 memory, the Transformer Engine, NVFP4 performance, cache, decoding, and scale-up systems address throughput, latency, long-context execution, and rack-scale deployment.

### Source excerpt

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....

## NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI

DevFeed: [NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI](<https://devfeed.tech/articles/nvidia-vera-cpu-olympus-cores-built-for-maximum-single-thread-performance-in-agentic-ai-6865.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-vera-cpu-olympus-cores-built-for-maximum-single-threaded-performance-in-agentic-ai/>)

Author: Praveen Menon

Published: 2026-07-21T18:00:00Z

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: [cpu](<https://devfeed.tech/topics/cpu.md>)

Tags: [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-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [code](<https://devfeed.tech/tags/code.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [sandboxes](<https://devfeed.tech/tags/sandboxes.md>), [tools](<https://devfeed.tech/tags/tools.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

The article explains how NVIDIA's Vera CPU and its Olympus cores are designed for the single-threaded performance, memory bandwidth, and predictable latency required by concurrent agentic AI workloads.

### Source excerpt

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with...

## NVIDIA NVLink: The Scale-Up Network for AI Factories

DevFeed: [NVIDIA NVLink: The Scale-Up Network for AI Factories](<https://devfeed.tech/articles/nvidia-nvlink-the-scale-up-network-for-ai-factories-6905.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nvlink-the-scale-up-network-for-ai-factories/>)

Author: Elizabeth Goodman

Published: 2026-07-20T15:46:28Z

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 Factory](<https://devfeed.tech/topics/ai-factory.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.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-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [collective](<https://devfeed.tech/tags/collective.md>), [communication](<https://devfeed.tech/tags/communication.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [production](<https://devfeed.tech/tags/production.md>), [scale](<https://devfeed.tech/tags/scale.md>), [spectrum-ethernet](<https://devfeed.tech/tags/spectrum-ethernet.md>), [spectrum-x](<https://devfeed.tech/tags/spectrum-x.md>), [speed](<https://devfeed.tech/tags/speed.md>), [systems](<https://devfeed.tech/tags/systems.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

NVIDIA NVLink is presented as a scale-up networking fabric for AI factories. It provides high-bandwidth, low-latency GPU-to-GPU communication for large AI inference, training, and parallel-computing workloads, with collective-operation acceleration and rack-level resiliency.

### Source excerpt

The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...

## 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...

## Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading

DevFeed: [Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading](<https://devfeed.tech/articles/reducing-high-bandwidth-memory-bottlenecks-in-jax-based-llm-training-with-host-offloading-6925.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/reducing-high-bandwidth-memory-bottlenecks-in-jax-based-llm-training-with-host-offloading/>)

Author: Tanya Lenz

Published: 2026-07-10T18:17:40Z

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: [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [blackwell](<https://devfeed.tech/tags/blackwell.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>), [featured](<https://devfeed.tech/tags/featured.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

This article explains how host offloading in JAX-based large language model training reduces GPU high-bandwidth memory pressure by moving selected activations to pinned host memory and streaming them back during the backward pass. It discusses activation-transfer overlap, NVIDIA Grace Blackwell and GB200 NVL72 systems, and experiments involving Llama 3.1 405B and DeepSeek-V3 671B.

### Source excerpt

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,...