# 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.