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NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut
NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.
In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.
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Quoting Paul Ford
Simon Willimon quotes Paul Ford arguing AI can write good software but cutting-edge work still demands human collaboration, craft, and judgment.
Simon Willimon highlights a passage from Paul Ford's essay 'A.I. Was Supposed to Give Us New Killer Apps. What Happened?'. Ford argues that while AI can write very good software, it also makes it easy to do someone else's job badly, which partly explains why many AI-driven projects fail. The quote reflects a broader industry reassessment of AI coding tools after initial fears that developer roles were obsolete.
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macOS 27 Golden Gate – Review
Ars Technica reviews macOS 27 Golden Gate, highlighting an unavoidable Apple Intelligence upgrade, new AFM 3 Core models, and dropped Intel Mac support.
macOS 27 Golden Gate delivers the first significant Apple Intelligence upgrade two years after launch, and the toggle to disable the AI features or delete downloaded models is gone. Apple Intelligence runs on a new AFM 3 Core model built in collaboration with Google, while the more capable AFM 3 Core Advanced requires an M3 chip and at least 12GB of RAM. The release drops all Intel Mac support, requiring Apple Silicon, with Sequoia security updates expected to end in fall 2027 and Tahoe's in 2028.
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I spent $4,000 on a robot dog from China
Hands-on review finds the $4,017 Unitree Go2 Pro robot dog affordable but impractical, as Unitree reaches a $34 billion valuation after its IPO.
Ars Technica reviewed the Unitree Go2 Pro quadruped, purchased for $4,017, finding it astonishingly cheap but of limited practical use; it collapsed from battery drain and heat (84°C internal temperature) on an uphill walk at 87°F. Unitree democratized quadruped research, sells humanoid robots from $13,500, and debuted on the Shanghai stock exchange on August 19 with shares rising over fivefold on day one, valuing the company at $34 billion. Its robots now face legal restrictions in the United States, and it competes with Boston Dynamics, whose Spot starts around $75,000.
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Facilitating AI integration with simplicity at scale
Jabil's SAP IT director says simplifying integration across 100+ sites in 30+ countries with SAP Integration Suite created the data backbone for AI.
In an MIT Technology Review Business Lab podcast produced in partnership with SAP, Jabil SAP IT director Harish Manohar described consolidating fragmented tools across more than 100 sites in over 30 countries using SAP Integration Suite. The manufacturer, with 140,000-plus employees and more than 400 top-brand customers, says a standardized data backbone enables real-time supply chain visibility and is a prerequisite for scaling predictive, AI-driven planning and forecasting. The company frames simplification-first modernization as a competitive advantage tied to measurable business value and operational resilience.
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Senior engineers are spending their week cleaning up AI-generated code
New Relic study finds AI-generated code doubles critical runtime issues, with senior engineers losing a third of their week to fixes.
A New Relic survey of U.S. technology leaders reports AI now writes the majority of shipped code, with senior SRE and DevOps engineers spending up to a third of their week triaging and refactoring it. A large majority of organizations had at least one AI-related production failure in the past six months, and roughly three in ten saw newly introduced security vulnerabilities. AI-generated code showed nearly twice as many critical runtime issues as peer-reviewed human-authored code, with gaps concentrated in edge cases, concurrency, deprecated APIs, and complex state changes. Most teams now prompt AI tools to embed logs and traces directly into generated code.
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