# Vera Rubin NVL72

Vera Rubin NVL72 is a rack-scale agentic AI supercomputer and data-center platform.

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## NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

DevFeed: [NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut](<https://devfeed.tech/articles/nvidia-vera-rubin-nvl72-delivers-leading-performance-in-mlperf-inference-v6-1-debut-31524.md>)

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

Author: Zhihan Jiang

Published: 2026-09-16T15:00:48Z

Content type: article

Language: en

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

Topics: [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [TensorRT-LLM](<https://devfeed.tech/topics/tensorrt-llm.md>)

Tags: [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mlperf](<https://devfeed.tech/tags/mlperf.md>), [networking](<https://devfeed.tech/tags/networking.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [software](<https://devfeed.tech/tags/software.md>), [tensorrt](<https://devfeed.tech/tags/tensorrt.md>), [tensorrt-llm](<https://devfeed.tech/tags/tensorrt-llm.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

NVIDIA reports MLPerf Inference v6.1 preview results for Vera Rubin NVL72 and GB300 NVL72 systems. Vera Rubin NVL72 delivered up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x higher throughput on DeepSeek-R1, while a four-rack GB300 NVL72 submission achieved 99% scaling efficiency. The results used vLLM, NVIDIA Dynamo, and TensorRT-LLM.

### Source excerpt

System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. [...]

## MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers

DevFeed: [MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers](<https://devfeed.tech/articles/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubin-s-first-peer-reviewed-numbers-31404.md>)

Original publisher: [Read original article](<https://www.storagereview.com/news/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubins-first-peer-reviewed-numbers>)

Author: Harold Fritts

Published: 2026-09-16T15:00:00Z

Content type: news

Language: en

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

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>)

Tags: [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [numbers](<https://devfeed.tech/tags/numbers.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [rag](<https://devfeed.tech/tags/rag.md>)

### AI overview

MLCommons published MLPerf Inference v6.1 with record participation, two new inference tests, and peer-reviewed results for several newly covered accelerators. The release reports a 5.7x improvement in the best per-accelerator DeepSeek-R1 server result compared with v5.1.

### Source excerpt

MLCommons has published MLPerf Inference v6.1, and the round sets a participation record with 30 submitting organizations and 486 datacenter and edge results. Two new tests join the suite: an End-to-End RAG pipeline for the datacenter and an Edge Agentic Inference benchmark for single-user devices, and the results carry the first peer-reviewed numbers for NVIDIA's The post MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers appeared first on StorageReview.com.

## 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...

## 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 [...]