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NVIDIA Releases AICR Recipe Family for Vera Rubin NVL72

On November 3, 2026, NVIDIA released the AI Cluster Runtime (AICR) recipe family for Vera Rubin NVL72, which uses reusable recipes to lock in tuning configurations, generate deployment packages, and validate running clusters, enabling Kubernetes environments to support Vera Rubin NVL72 from Day 1. This recipe series locks in configurations including the R615.23 open-source kernel module driver, DRA driver v0.5.0 (including ComputeDomains), and the 64k-page kernel of Ubuntu 26.04, and completed end-to-end validation on a reference cluster using GPU Operator v26.7.0, with cross-node NCCL all-reduce reaching 744.71 GB/s bus bandwidth at 16 GiB. Early chip validation also found that go-nvlib did not map the Rubin architecture value, causing the DRA driver's kubelet plugin to crash and restart; the relevant fix has entered the official release of DRA driver v0.5.0. There are currently no further developments.

NVIDIA releases AICR recipe family for Vera Rubin NVL72

On November 3, 2026, NVIDIA released the AI Cluster Runtime (AICR) recipe family for Vera Rubin NVL72, which uses reusable recipes to lock in tuning configurations, generate deployment packages, and validate running clusters, enabling Kubernetes environments to support Vera Rubin NVL72 from Day 1. This recipe series locks in configurations including the R615.23 open-source kernel module driver, DRA driver v0.5.0 (including ComputeDomains), and the 64k-page kernel of Ubuntu 26.04, and completed end-to-end validation on a reference cluster using GPU Operator v26.7.0, with cross-node NCCL all-reduce reaching 744.71 GB/s bus bandwidth at 16 GiB. Early chip validation also found that go-nvlib did not map the Rubin architecture value, causing the DRA driver's kubelet plugin to crash and restart; the relevant fix has entered the official release of DRA driver v0.5.0. There are currently no further developments.

View story:NVIDIA Releases AICR Recipe Family for Vera Rubin NVL72

NVIDIA Expands Cosmos Platform Training Tools and Data Blueprints

On October 22, 2026, NVIDIA announced the expansion of the Cosmos platform, adding training tools, simulation checkpoints, and Cosmos Data Factory blueprints to help developers build world models. According to information released by the NVIDIA Newsroom, Cosmos Data Factory provides five cloud-native blueprints, now available through GitHub, Microsoft Azure, Nebius, and CoreWeave; Porsche is using it to build a shared autonomous driving data flywheel, which is expected to reduce repetitive real-vehicle testing by up to 90% and shorten development cycles by up to 85%. Cosmos Framework's training, optimization, and deployment tools are being extended in the form of agent skills to open-source models such as GR00T, Alpamayo, QWEN, and Pi0.5, and the Cosmos 3 NIM microservice delivers up to 2x faster inference under validated configurations. There are currently no further developments.

Nvidia responds to report that U.S. intelligence agencies surveilled people close to Jensen Huang

On October 30, 2026, Nvidia responded to an investigative report by The New York Times. The report said that when U.S. intelligence agencies were investigating the chip transshipment issue, they surveilled individuals close to Nvidia CEO Jensen Huang and identified Alice Huang, an executive at the Singapore cloud service provider Megaspeed, as a suspected Chinese government intelligence officer. An Nvidia spokesperson said that Megaspeed is merely a regional cloud customer of Nvidia, that it is normal for Jensen Huang to communicate with thousands of customers each year, and that allegations that Megaspeed evaded export controls were thoroughly investigated and clarified last year, and that customers cannot access Nvidia's trade secrets or related national security information. Nvidia also said that if the U.S. government believes anyone poses a threat to American industry, it will inform the company.

Nvidia responds to report that U.S. intelligence agencies surveilled people close to Jensen Huang

On October 30, 2026, Nvidia responded to an investigative report by The New York Times. The report said that when U.S. intelligence agencies were investigating the chip transshipment issue, they surveilled individuals close to Nvidia CEO Jensen Huang and identified Alice Huang, an executive at the Singapore cloud service provider Megaspeed, as a suspected Chinese government intelligence officer. An Nvidia spokesperson said that Megaspeed is merely a regional cloud customer of Nvidia, that it is normal for Jensen Huang to communicate with thousands of customers each year, and that allegations that Megaspeed evaded export controls were thoroughly investigated and clarified last year, and that customers cannot access Nvidia's trade secrets or related national security information. Nvidia also said that if the U.S. government believes anyone poses a threat to American industry, it will inform the company.

View story:Nvidia responds to report that U.S. intelligence agencies surveilled people close to Jensen Huang

NVIDIA Releases Open-Weight Speech Transcription Model Nemotron 3.5 Transcribe

Nvidia in talks to guarantee debt for Nscale data center construction

People familiar with the matter say Nvidia is in talks to provide a multi-billion-dollar debt credit guarantee for AI cloud service provider Nscale, to be used to build a 460-megawatt data center in West Virginia; the facility will be equipped entirely with Nvidia Vera Rubin GPUs and leased to Anthropic. The agreement takes the form of a residual-value guarantee on the data center assets themselves, rather than on the lease or the chips, which would allow Nscale to obtain investment-grade financing; if Nscale fails to pay construction costs on schedule, Nvidia will pay on its behalf and temporarily take over the facility. According to a report by The Information confirmed by Bloomberg, under the residual-value guarantee arrangement, if Nscale defaults on construction payments, Nvidia will temporarily take over the facility and help find a buyer and new tenants. Nscale's IPO filing shows that Anthropic has signed a $44.6 billion contract for the first building of the project; that building has a capacity of 460 megawatts and is expected to use Nvidia's next-generation Vera Rubin chips, and the two sides are also in talks on another building worth about $45 billion. Nvidia and Nscale have not yet publicly confirmed the guarantee arrangement.

Nvidia in talks to guarantee debt for Nscale data center construction

People familiar with the matter say Nvidia is in talks to provide a multi-billion-dollar debt credit guarantee for AI cloud service provider Nscale, to be used to build a 460-megawatt data center in West Virginia; the facility will be equipped entirely with Nvidia Vera Rubin GPUs and leased to Anthropic. The agreement takes the form of a residual-value guarantee on the data center assets themselves, rather than on the lease or the chips, which would allow Nscale to obtain investment-grade financing; if Nscale fails to pay construction costs on schedule, Nvidia will pay on its behalf and temporarily take over the facility. According to a report by The Information confirmed by Bloomberg, under the residual-value guarantee arrangement, if Nscale defaults on construction payments, Nvidia will temporarily take over the facility and help find a buyer and new tenants. Nscale's IPO filing shows that Anthropic has signed a $44.6 billion contract for the first building of the project; that building has a capacity of 460 megawatts and is expected to use Nvidia's next-generation Vera Rubin chips, and the two sides are also in talks on another building worth about $45 billion. Nvidia and Nscale have not yet publicly confirmed the guarantee arrangement.

View story:Nvidia in talks to guarantee debt for Nscale data center construction

Ant Ling and NVIDIA Optimize Single-GPU Inference for Ling-3.0-flash

On October 29, 2026, Ant Ling, in collaboration with the NVIDIA AI team, optimized the inference of the open-weight model Ling-3.0-flash on a single NVIDIA DGX Spark using three techniques. On 1,319 GSM8K problems, decoding throughput increased from 37.81 tok/s to 53.89 tok/s (+42.5%), and average latency dropped from 15.21s to 11.49s (-24.5%), with no statistically detectable change in accuracy. There are currently no further developments.

Siemens and Nvidia partner to develop an AI-driven PCB inspection workflow

Siemens and Nvidia have partnered to develop an AI-driven PCB inspection workflow that combines Teamcenter, Process Preparation X, and Nvidia Cosmos to complete AOI inspection preparation and calibration before physical production with the help of digital twins and synthetic images. The solution was demonstrated at Siemens' Erlangen electronics factory, with the goal of reducing the manual engineering effort of AOI program generation by up to 85%, cutting false positives by 40%, and raising the first-pass yield by more than 5%. No further progress has been disclosed at present.

NVIDIA releases open-weight speech transcription model Nemotron 3.5 Transcribe

View story:NVIDIA Releases Open-Weight Speech Transcription Model Nemotron 3.5 Transcribe

Siemens and Nvidia partner to develop AI-driven PCB inspection workflow

Siemens and Nvidia have partnered to develop an AI-driven PCB inspection workflow that combines Teamcenter, Process Preparation X, and Nvidia Cosmos to complete AOI inspection preparation and calibration before physical production with the help of digital twins and synthetic images. The solution was demonstrated at Siemens' Erlangen electronics factory, with the goal of reducing the manual engineering effort of AOI program generation by up to 85%, cutting false positives by 40%, and raising the first-pass yield by more than 5%. No further progress has been disclosed at present.

View story:Siemens and Nvidia partner to develop an AI-driven PCB inspection workflow

Ant Ling and NVIDIA optimize single-GPU inference for Ling-3.0-flash

On October 29, 2026, Ant Ling, in collaboration with the NVIDIA AI team, optimized the inference of the open-weight model Ling-3.0-flash on a single NVIDIA DGX Spark using three techniques. On 1,319 GSM8K problems, decoding throughput increased from 37.81 tok/s to 53.89 tok/s (+42.5%), and average latency dropped from 15.21s to 11.49s (-24.5%), with no statistically detectable change in accuracy. There are currently no further developments.

View story:Ant Ling and NVIDIA Optimize Single-GPU Inference for Ling-3.0-flash