Search “NVIDIA”

Databricks and NVIDIA Bring RFD4-Proteina to Genesis Workbench

Databricks and NVIDIA plan to integrate NVIDIA BioNeMo's open-source protein design model RFD4-Proteina into Databricks' Genesis Workbench. According to a Databricks blog post dated October 21, 2026, the model will run on the generally available AI Runtime serverless NVIDIA GPU compute. RFD4-Proteina can simultaneously generate protein sequences and all-atom structures, and supports optional reward-guided search that steers candidate designs toward target properties such as thermostability and binding affinity. Genesis Workbench includes two built-in RFD4-Proteina workflows, Antibody Design and Vaccine Design, and can be extended through Genie Code and NVIDIA BioNeMo Agent Toolkit skills. No further progress has been disclosed at this time.

Databricks and NVIDIA bring RFD4-Proteina to Genesis Workbench

Databricks and NVIDIA plan to integrate NVIDIA BioNeMo's open-source protein design model RFD4-Proteina into Databricks' Genesis Workbench. According to a Databricks blog post dated October 21, 2026, the model will run on the generally available AI Runtime serverless NVIDIA GPU compute. RFD4-Proteina can simultaneously generate protein sequences and all-atom structures, and supports optional reward-guided search that steers candidate designs toward target properties such as thermostability and binding affinity. Genesis Workbench includes two built-in RFD4-Proteina workflows, Antibody Design and Vaccine Design, and can be extended through Genie Code and NVIDIA BioNeMo Agent Toolkit skills. No further progress has been disclosed at this time.

View story:Databricks and NVIDIA Bring RFD4-Proteina to Genesis Workbench

Nvidia releases open-weight models for enterprise agents and PCs

On October 19, 2026, Nvidia released open-weight models for always-on agents and edge computing. According to TechCrunch AI, the Nemotron 3.5 Super VL is a 120-billion-parameter enterprise-grade agent model, which Nvidia says can achieve accuracy comparable to Nemotron 3 Ultra on instruction-following and tool-calling tasks; two lightweight Bolt models are designed to run locally on AI PCs equipped with RTX Spark chips and on DGX Spark workstations. The report said Super VL and Nemotron 3.5 Bolt 74B-A9B 24GB are already available, and the smallest Bolt E4B will launch later this month. On October 21, Nvidia announced an expansion of its open model portfolio covering language, robotics, biology and quantum computing, positioned around European sovereign intelligence; the portfolio includes Nemotron 3.5 Super VL and Nemotron 3.5 Bolt, among others. The report said Super VL is now available on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, and Bolt E4B 4GB is expected to be available on October 23.

English translation is being prepared.

On October 19, 2026, Nvidia released open-weight models for always-on agents and edge computing. According to TechCrunch AI, the Nemotron 3.5 Super VL is a 120-billion-parameter enterprise-grade agent model, which Nvidia says can achieve accuracy comparable to Nemotron 3 Ultra on instruction-following and tool-calling tasks; two lightweight Bolt models are designed to run locally on AI PCs equipped with RTX Spark chips and on DGX Spark workstations. The report said Super VL and Nemotron 3.5 Bolt 74B-A9B 24GB are already available, and the smallest Bolt E4B will launch later this month. On October 21, Nvidia announced an expansion of its open model portfolio covering language, robotics, biology and quantum computing, positioned around European sovereign intelligence; the portfolio includes Nemotron 3.5 Super VL and Nemotron 3.5 Bolt, among others. The report said Super VL is now available on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, and Bolt E4B 4GB is expected to be available on October 23.

View story:Nvidia releases open-weight models for enterprise agents and PCs

NVIDIA announces global telecom operators are turning networks into distributed intelligent platforms

On October 21, 2026, NVIDIA announced at GTC Berlin that multiple telecom operators worldwide are extending NVIDIA accelerated computing to central offices, metro sites and base stations, turning networks into distributed intelligent platforms. Reports the same day show that T-Mobile, together with NVIDIA and Nokia, completed an AI-RAN call on a commercial network in Bellevue, Washington; BT, Deutsche Telekom, Elisa and Orange are conducting live network trials based on Nokia's CUDA-accelerated anyRAN software and NVIDIA Aerial RAN Computer. NVIDIA also launched two configurations, ARC MGX 2U and MGX 4U, for high-capacity sites, and Nokia and Cisco have integrated the Nemotron open model into their respective network automation software. Other reports say nearly 20 operators have announced AI-RAN collaborations based on Nokia anyRAN software and NVIDIA Aerial RAN Computer; BT and Deutsche Telekom plan to conduct live network trials in 2027, and Elisa is already running voice, mobile data and video analytics simultaneously in a live 5G spectrum environment.

English translation is being prepared.

On October 21, 2026, NVIDIA announced at GTC Berlin that multiple telecom operators worldwide are extending NVIDIA accelerated computing to central offices, metro sites and base stations, turning networks into distributed intelligent platforms. Reports the same day show that T-Mobile, together with NVIDIA and Nokia, completed an AI-RAN call on a commercial network in Bellevue, Washington; BT, Deutsche Telekom, Elisa and Orange are conducting live network trials based on Nokia's CUDA-accelerated anyRAN software and NVIDIA Aerial RAN Computer. NVIDIA also launched two configurations, ARC MGX 2U and MGX 4U, for high-capacity sites, and Nokia and Cisco have integrated the Nemotron open model into their respective network automation software. Other reports say nearly 20 operators have announced AI-RAN collaborations based on Nokia anyRAN software and NVIDIA Aerial RAN Computer; BT and Deutsche Telekom plan to conduct live network trials in 2027, and Elisa is already running voice, mobile data and video analytics simultaneously in a live 5G spectrum environment.

View story:NVIDIA announces global telecom operators are turning networks into distributed intelligent platforms

English translation is being prepared.

English translation is being prepared.

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NVIDIA Announces Expansion of the Isaac Open Robot Development Platform

On October 21, 2026, NVIDIA announced at GTC Berlin the expansion of the Isaac open robotics development platform, adding a reference humanoid robot, datasets, and a runtime, and updating the robot learning framework. On the same day, Isaac Lab 3.0 became generally available, and the SimReady Central Library and the Human 1M dataset went live on Hugging Face. Agile Robots' Agile ONE SN, based on Isaac GR00T and Jetson Thor, stands 1.40 meters tall with 31 degrees of freedom and is planned for launch in early 2027. According to a report the same day, NVIDIA also launched the SimReady Central open 3D asset library on Hugging Face; the assets are in OpenUSD format with simulation attributes such as mass, collision geometry, and friction, and developers and AI agents can use NVIDIA USD Search to retrieve assets in natural language and assemble them into Isaac Sim scenes. The platform expansion has now been implemented with the above releases.

NVIDIA announces expansion of the Isaac open robotics development platform

On October 21, 2026, NVIDIA announced at GTC Berlin the expansion of the Isaac open robotics development platform, adding a reference humanoid robot, datasets, and a runtime, and updating the robot learning framework. On the same day, Isaac Lab 3.0 became generally available, and the SimReady Central Library and the Human 1M dataset went live on Hugging Face. Agile Robots' Agile ONE SN, based on Isaac GR00T and Jetson Thor, stands 1.40 meters tall with 31 degrees of freedom and is planned for launch in early 2027. According to a report the same day, NVIDIA also launched the SimReady Central open 3D asset library on Hugging Face; the assets are in OpenUSD format with simulation attributes such as mass, collision geometry, and friction, and developers and AI agents can use NVIDIA USD Search to retrieve assets in natural language and assemble them into Isaac Sim scenes. The platform expansion has now been implemented with the above releases.

View story:NVIDIA Announces Expansion of the Isaac Open Robot Development Platform

NVIDIA announces expansion of AI infrastructure capacity with supply chain partners

On October 21, 2026, NVIDIA announced at GTC Berlin the expansion of global manufacturing capacity with supply chain partners including Amkor, IBIDEN, KYEC, Samsung Electro-Mechanics, SPIL and Unimicron. According to information released by NVIDIA Newsroom that day, it plans to increase substrate capacity 2.5 times over the next three years; Amkor plans to increase advanced packaging capacity by more than 300% by the end of 2028; NVIDIA is providing KYEC with a $1.1 billion capacity reservation prepayment to support its new test facility in the United States, targeting mass production in April 2027; SPIL plans to increase co-packaged optics optical engine capacity 15 times by mid-2027 and seeks to double it again by the end of 2028. The Elec reported on October 22 that the agreements with Ibiden, Samsung Electro-Mechanics and Unimicron will expand semiconductor packaging substrate capacity 2.5 times over the next three years, and that Ibiden's Ono plant in Japan will triple its capacity and reach full production in the first quarter of 2028; the report also said NVIDIA prepaid KYEC $1.1 billion, doubling its total test capacity in 2027 compared with 2026. There are currently no facts of subsequent progress.

NVIDIA announces expansion of AI infrastructure capacity with supply chain partners

On October 21, 2026, NVIDIA announced at GTC Berlin the expansion of global manufacturing capacity with supply chain partners including Amkor, IBIDEN, KYEC, Samsung Electro-Mechanics, SPIL and Unimicron. According to information released by NVIDIA Newsroom that day, it plans to increase substrate capacity 2.5 times over the next three years; Amkor plans to increase advanced packaging capacity by more than 300% by the end of 2028; NVIDIA is providing KYEC with a $1.1 billion capacity reservation prepayment to support its new test facility in the United States, targeting mass production in April 2027; SPIL plans to increase co-packaged optics optical engine capacity 15 times by mid-2027 and seeks to double it again by the end of 2028. The Elec reported on October 22 that the agreements with Ibiden, Samsung Electro-Mechanics and Unimicron will expand semiconductor packaging substrate capacity 2.5 times over the next three years, and that Ibiden's Ono plant in Japan will triple its capacity and reach full production in the first quarter of 2028; the report also said NVIDIA prepaid KYEC $1.1 billion, doubling its total test capacity in 2027 compared with 2026. There are currently no facts of subsequent progress.

View story:NVIDIA announces expansion of AI infrastructure capacity with supply chain partners

NVIDIA Expands Nemotron Retriever Series and Releases Multiple Open Models

On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, releasing multiple open models covering embedding, reranking, and document parsing. The models introduced include Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and Nemotron 3.5 Parse with 631 million parameters, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100. Open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM and through Baseten, DeepInfra, and FriendliAI.

View story:NVIDIA Expands Nemotron Retriever Series and Releases Multiple Open Models

NVIDIA and Google Cloud announce the launch of the first A5X instances

On October 21, 2026, NVIDIA and Google Cloud announced at GTC Berlin that the first A5X instances equipped with NVIDIA Vera Rubin NVL72 are already running at one customer. The two companies plan to make them generally available in November 2026 and to expand the instances to Europe. A5X combines ConnectX-9 SuperNICs, Google Cloud's Virgo network, and fourth-generation liquid cooling, targeting large-scale agentic inference and reinforcement learning workloads; Google Cloud says it plans to scale this infrastructure to as many as 960,000 Rubin GPUs. The first customer is Ineffable Intelligence, a London-based company founded by David Silver, which will use it for large-scale reinforcement learning; WPP also demonstrated a creative agent architecture based on NVIDIA Nemotron models and OpenShell on A5X.

English translation is being prepared.

On October 21, 2026, NVIDIA and Google Cloud announced at GTC Berlin that the first A5X instances equipped with NVIDIA Vera Rubin NVL72 are already running at one customer. The two companies plan to make them generally available in November 2026 and to expand the instances to Europe. A5X combines ConnectX-9 SuperNICs, Google Cloud's Virgo network, and fourth-generation liquid cooling, targeting large-scale agentic inference and reinforcement learning workloads; Google Cloud says it plans to scale this infrastructure to as many as 960,000 Rubin GPUs. The first customer is Ineffable Intelligence, a London-based company founded by David Silver, which will use it for large-scale reinforcement learning; WPP also demonstrated a creative agent architecture based on NVIDIA Nemotron models and OpenShell on A5X.

View story:NVIDIA and Google Cloud announce the launch of the first A5X instances

Nvidia adds Agent Knowledge Foundation to Agent Toolkit

At GTC Berlin, Nvidia added the Agent Knowledge Foundation to its Agent Toolkit. The component packages the Nemotron Retriever models, a new Auto-Ontology library, the Kumo family of models for structured data, and CUDA-X data processing libraries; data remains in place and is governed by enterprises' existing permissions. Nvidia says Nemotron Retriever improved search relevance by 65% in tests in the open-source OpenSearch environment, SAP's fine-tuned reranker runs nearly three times faster, and ServiceNow's reranking offers better quality with nearly 25% lower latency. Auto-Ontology provides a REST API and an MCP server for agents to query. Nvidia says it does not compete with data platforms such as Snowflake, but partners with them. The news was reported by The New Stack; there are no subsequent developments yet.

View story:Nvidia adds Agent Knowledge Foundation to Agent Toolkit

Mistral and NVIDIA partner to develop AI-native engineering workflows

On October 21, 2026, Mistral and NVIDIA announced a partnership to develop AI-native engineering workflows, combining Mistral Large 4 with NVIDIA CUDA-X, Warp, and Omniverse to accelerate engineering simulation and close the loop between simulation, model development, and design. The two parties also plan to launch public benchmarks and open datasets for physical models, and to establish evaluations for agentic AI in CAE and EDA scenarios, examining tool calling, multi-step process completion, and execution time and compute cost. According to information released by Mistral AI, Mistral Large 4 has approximately 1 trillion total parameters and 49 billion activated parameters, was trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's data center in Europe, and the weights are planned for release at the end of October. The Decoder reported the same day that the partnership targets industries including aerospace, automotive, and semiconductors, and that Mistral plans to expand compute to 1 gigawatt by 2030. There is currently no further progress.

Mistral and NVIDIA partner to develop AI-native engineering workflows

On October 21, 2026, Mistral and NVIDIA announced a partnership to develop AI-native engineering workflows, combining Mistral Large 4 with NVIDIA CUDA-X, Warp, and Omniverse to accelerate engineering simulation and close the loop between simulation, model development, and design. The two parties also plan to launch public benchmarks and open datasets for physical models, and to establish evaluations for agentic AI in CAE and EDA scenarios, examining tool calling, multi-step process completion, and execution time and compute cost. According to information released by Mistral AI, Mistral Large 4 has approximately 1 trillion total parameters and 49 billion activated parameters, was trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's data center in Europe, and the weights are planned for release at the end of October. The Decoder reported the same day that the partnership targets industries including aerospace, automotive, and semiconductors, and that Mistral plans to expand compute to 1 gigawatt by 2030. There is currently no further progress.

View story:Mistral and NVIDIA partner to develop AI-native engineering workflows

NVIDIA Expands Nemotron Retriever Series and Releases Multiple Open Models

On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, releasing multiple open models covering embedding, reranking, and document parsing. The models introduced include Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and Nemotron 3.5 Parse with 631 million parameters, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100. Open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM and through Baseten, DeepInfra, and FriendliAI.

Nvidia adds Agent Knowledge Foundation to Agent Toolkit

At GTC Berlin, Nvidia added the Agent Knowledge Foundation to its Agent Toolkit. The component packages the Nemotron Retriever models, a new Auto-Ontology library, the Kumo family of models for structured data, and CUDA-X data processing libraries; data remains in place and is governed by enterprises' existing permissions. Nvidia says Nemotron Retriever improved search relevance by 65% in tests in the open-source OpenSearch environment, SAP's fine-tuned reranker runs nearly three times faster, and ServiceNow's reranking offers better quality with nearly 25% lower latency. Auto-Ontology provides a REST API and an MCP server for agents to query. Nvidia says it does not compete with data platforms such as Snowflake, but partners with them. The news was reported by The New Stack; there are no subsequent developments yet.