# GB300 NVL72

Published articles for GB300 NVL72.

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

## Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding

DevFeed: [Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding](<https://devfeed.tech/articles/experiment-with-qwen3-8-flash-next-on-nvidia-gb300-nvl72-for-agentic-coding-6819.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/experiment-with-qwen3-8-flash-next-on-nvidia-gb300-nvl72-for-agentic-coding/>)

Author: Michelle Horton

Published: 2026-08-26T17:07:12Z

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: [qwen](<https://devfeed.tech/topics/qwen.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [TensorRT-LLM](<https://devfeed.tech/topics/tensorrt-llm.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [rust-ai](<https://devfeed.tech/topics/rust-ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

This NVIDIA developer article introduces Qwen3.8-Flash-Next, a multimodal mixture-of-experts model released by Alibaba for experimentation and evaluation. It explains the model's long-context hybrid architecture, including Gated DeltaNet and Qwen Sparse Attention, and discusses reported efficiency improvements for million-token workloads. The article also covers inference support through SGLang, vLLM, TensorRT-LLM, and NVIDIA NeMo, plus performance on the NVIDIA GB300 NVL72 platform.

### Source excerpt

Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate. It's...

## How XPUs Meet a World-Class AI Factory

DevFeed: [How XPUs Meet a World-Class AI Factory](<https://devfeed.tech/articles/how-xpus-meet-a-world-class-ai-factory-6958.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/nvlink-fusion-xpu-ai-factory/>)

Author: Jesse Clayton

Published: 2026-08-24T15:00:54Z

Content type: article

Language: en

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

Topics: [AI Factory](<https://devfeed.tech/topics/ai-factory.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [Network](<https://devfeed.tech/topics/network.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [moe](<https://devfeed.tech/topics/moe.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [ethernet](<https://devfeed.tech/tags/ethernet.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia-dsx](<https://devfeed.tech/tags/nvidia-dsx.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [software](<https://devfeed.tech/tags/software.md>), [time](<https://devfeed.tech/tags/time.md>), [xpu](<https://devfeed.tech/tags/xpu.md>)

### AI overview

The article explains how NVLink Fusion combines custom XPUs with NVIDIA's established AI infrastructure to help build semi-custom AI factories. It focuses on scale-up networking, performance, resiliency, telemetry, platform maturity, and the economics of large-scale AI workloads.

### Source excerpt

To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider [...]

## Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents

DevFeed: [Up to 30x More Work Per Watt: NVIDIA Vera Rubin NVL72 Sets a New Efficiency Standard for AI Agents](<https://devfeed.tech/articles/up-to-30x-more-work-per-watt-nvidia-vera-rubin-nvl72-sets-a-new-efficiency-standard-for-ai-agents-6964.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/vera-rubin-nvl72-efficiency-ai-agents/>)

Author: Shruti Koparkar

Published: 2026-08-24T15:00:19Z

Content type: article

Language: en

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

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [GB300 NVL72](<https://devfeed.tech/topics/gb300-nvl72.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia-blackwell](<https://devfeed.tech/tags/nvidia-blackwell.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [performance](<https://devfeed.tech/tags/performance.md>), [software](<https://devfeed.tech/tags/software.md>), [think-smart](<https://devfeed.tech/tags/think-smart.md>)

### AI overview

NVIDIA reports that Vera Rubin NVL72 systems deliver up to 30x higher throughput per megawatt than GB300 NVL72 on agentic workloads measured with the SemiAnalysis AgentX workload. The article attributes the efficiency challenge to long, variable agent workflows involving tool calls, accumulated context, and sub-agents.

### Source excerpt

According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a [...]

## 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...

## Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72

DevFeed: [Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72-6938.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72/>)

Author: Michelle Horton

Published: 2026-08-12T18:23:13Z

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 Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cache](<https://devfeed.tech/tags/cache.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model](<https://devfeed.tech/tags/model.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [routing](<https://devfeed.tech/tags/routing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

The article describes serving Alibaba's open-weight Qwen3.8-2.4T-A95B model on NVIDIA GB300 NVL72 systems for large-scale reasoning and agentic workloads.

### Source excerpt

Alibaba released the open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model, bringing near-frontier capabilities to the open...

## NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure

DevFeed: [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure](<https://devfeed.tech/articles/nvidia-exemplar-cloud-lessons-for-unlocking-full-performance-on-ai-infrastructure-6891.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-exemplar-cloud-lessons-for-unlocking-full-performance-on-ai-infrastructure/>)

Author: Elizabeth Goodman

Published: 2026-07-30T16:00:00Z

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: [debugging](<https://devfeed.tech/topics/debugging.md>), [Processes](<https://devfeed.tech/topics/processes.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-services](<https://devfeed.tech/tags/cloud-services.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dgx-cloud](<https://devfeed.tech/tags/dgx-cloud.md>), [diagnostics](<https://devfeed.tech/tags/diagnostics.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [hopper](<https://devfeed.tech/tags/hopper.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [linux](<https://devfeed.tech/tags/linux.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [training](<https://devfeed.tech/tags/training.md>)

### 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...

## Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72

DevFeed: [Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72-6939.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72/>)

Author: Kirthi Devleker

Published: 2026-07-21T18:30:00Z

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: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [networking](<https://devfeed.tech/topics/networking.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [collective](<https://devfeed.tech/tags/collective.md>), [communication](<https://devfeed.tech/tags/communication.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [train](<https://devfeed.tech/tags/train.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains how NVIDIA GB300 NVL72 achieved a world record for DeepSeek-V3 671B mixture-of-experts pre-training. It focuses on the communication demands of MoE models, including all-to-all traffic between GPUs, and the need for tightly coupled scale-up and predictable scale-out networking to sustain delivered training performance.

### Source excerpt

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...