New Quick-Start Programme Speeds Order-to-Ship to 15 Days for Validated, Right-Sized AI Factory Configurations, Helping Customers Move from AI Strategy to Operations Faster

Lenovo announced Lenovo AI Express, a new accelerated path to production AI that removes deployment complexities by giving customers of all sizes quicker return on their AI investment.

According to Lenovo’s 2026 CIO Playbook, organisations moving from AI experimentation to scaled execution are expecting an average $2.78 return for every $1 invested, with 94% of enterprise respondents anticipating positive returns. Yet long lead times, integration complexity, and other readiness barriers can slow the path to realising that value. In collaboration with NVIDIA, Lenovo AI Express addresses these barriers, combining rapid access to AI capacity with validated, right-sized configurations and services that simplify procurement, deployment and operations. Customers can start with a solution sized for today’s workloads and budgets, then scale as agents, models, users, data and inference demands grow – with order-to-ship accelerating to 15 business days for eligible configurations¹.

“Organisations are navigating today’s complexity and constraints to get AI into production faster, looking for an approach that fits their current needs while giving them the ability to scale as those needs evolve,” said Ashley Gorakhpurwalla, President, Infrastructure Solutions Group, Lenovo. “Lenovo AI Express gives our customers an accelerated path to deploying AI infrastructure to fit their specifications while mitigating the risk of overbuilding or costly delays.” 

 “For customers looking for validated AI solutions that can be sourced, built, and delivered quickly, Lenovo AI Express provides a predictable way to configure, purchase and receive the infrastructure. CIO’s can start their AI projects with the use case that matters now, then expand as their workloads, data requirements and AI maturity develop,” said Per Overgaard, General Manager, Infrastructure Solutions Group, Europe and META, Lenovo. “Lenovo is helping customers make that progression with the speed they need, the regional support they value, and the control their business demands.”

Start Faster, Scale with Confidence

Lenovo AI Express is a streamlined path to the Lenovo Hybrid AI Factory with NVIDIA, helping customers achieve more predictable deployment and access to production capacity, backed by Lenovo’s highly-ranked secure supply chain. They can choose from three validated quick-start configurations based on their model, user and throughput requirements matched to each level of AI demand

• Small – From 15 Days¹: Supports focused AI inferencing for tens of users at 30+TPS², with average model sizes from 7B – 70B, built on Lenovo ThinkSystem SR650a V4 and powered by two NVIDIA RTX 6000 PRO Blackwell Server Edition GPUs. 
• Medium – From 20 Days¹:  Supports higher-throughput inferencing and agentic AI for hundreds of users at 30+ TPS², with average model sizes from 70B–400B, built on Lenovo ThinkSystem SR675 V3 and powered by eight  NVIDIA RTX 6000 PRO Blackwell Server Edition GPUs.
• Large – From 25 Days¹: Supports full-scale generative AI models serving thousands of users² across a wide range of workloads and interactivity levels, with up to a trillion parameters and enterprise-scale deployments, built on Lenovo ThinkSystem SR680a V4 and powered by NVIDIA HGX B300.

Across configurations, Lenovo AI Express also supports the latest CPU technologies from AMD and Intel, giving customers a choice of validated technologies to meet their AI workload requirements. Customers can extend their chosen configuration with enterprise AI software based on their requirements, including Red Hat AI Factory with NVIDIA or NVIDIA AI Enterprise software. For additional AI data resilience, customers can include Veeam® Kasten to protect AI applications, data, models and pipelines. 

“As AI agents reshape every industry, enterprises need purpose-built infrastructure that can efficiently turn their data into intelligence,” said Chris Marriott, vice president of enterprise platforms at NVIDIA. “Lenovo AI Express combines Lenovo’s validated systems with NVIDIA accelerated computing, networking and AI software into ready-to-ship solutions, giving organisations a faster path to deploy AI factories and scale as their workloads and demand for intelligence grow.”

Deploy and Operate with Confidence

Lenovo AI Express extends beyond rapid access to capacity with a full range of services that accelerate deployment and simplify the AI factory lifecycle, from identifying uses cases and validating ROI through proofs of concepts to optimising GPU environments for performance, utilisation and cost efficiency. New Premier Support Plus for Servers add proactive and predictive support capabilities designed to identify issues earlier and help maintain operational continuity for mission-critical environments. Together, these capabilities provide everything customers need to move AI use cases into production using their own data.

Scale with Lenovo and Partner Ecosystem 

Lenovo is also expanding access to Lenovo AI Express through its global partner ecosystem, giving channel partners a simpler, faster and more predictable way to identify and pursue customer AI opportunities. Through the Lenovo 360 framework, partners can bring validated Lenovo Hybrid AI Factory configurations and capabilities to customers with support throughout the sales process.  Availability, eligibility and ordering processes vary by region, with additional guidance provided locally.

Building on the success of Lenovo Top Choice Express, AI Express brings the same combination of configuration flexibility, clearer pricing and more predictable fulfilment to enterprise AI. It gives partners a more repeatable route to market, helping them turn customer interest into a validated, deployable solution without navigating a complex, bespoke process each time.

“Top Choice Express has already helped partners respond to mainstream infrastructure demand with a more predictable combination of configuration flexibility, pricing and fulfilment, and AI Express extends that value proposition into enterprise AI.” says Ilenia Lupi, Europe Channel Leader, Lenovo. “Our partners meet demand with greater confidence as they offer their customers a clear starting point for AI with a practical route to deployment.”

“Lenovo Top Choice Express helps us bundle the right Lenovo devices into that wider solution with greater consistency and certainty,” said Rohil Dattani, Managing Director, Ballicom. “In an unpredictable market, that stability supports business continuity for Ballicom and a smoother experience for the customers we serve.”

The post Lenovo AI Express with NVIDIA Accelerates Path to Hybrid AI Factory in Weeks appeared first on Tech Scene ZA.

Open Software Platform and Reference System Design Brings Together Industry, Researchers and Public-Sector Organizations to Set Safer Boundaries for AI Agents, Share Best Practices and Foster International Cooperation to Raise the Bar for Safer AI Agent Deployment

News Summary:

• NVIDIA Open Agent Safety Platform consists of NVIDIA OpenShell open source software and the NVIDIA Sentry reference system design that enables full-stack governance and control across software and the hardware, compute and robotics systems that run agents.
• OpenShell software provides a secure runtime boundary that traces all actions and enforces policy as agents run on NVIDIA Vera CPUs. As open source software, OpenShell can be extended to work with third-party compute platforms, including those from Arm and Intel.
• Sentry adds an out-of-band watchdog that runs on NVIDIA BlueField-4 DPUs to continuously monitor agent behavior. Sentry can quarantine agents that attempt to move outside their boundaries in milliseconds.
• Industry leaders from across the AI ecosystem are joining NVIDIA to strengthen AI safety for every industry across the full stack of infrastructure, software, models and robotics — including Anthropic, Cisco, CrowdStrike, Dell Technologies, Figure, HPE, Hugging Face, JPMorganChase, Microsoft, Palantir, Palo Alto Networks, Perplexity, Red Hat, Salesforce, SAP, Scale AI, ServiceNow and SpaceXAI.

NVIDIA today announced NVIDIA Open Agent Safety Platform, an open software platform and reference system design to strengthen AI security from agent testing to deployment, with full-stack governance and control across software and the hardware, compute and robotics systems that run agents.

Recent security incidents have underscored the need to equip organizations with open, customizable tools that enforce more control over long-running agents. Across these incidents, the pattern is the same — the agent circumvented security controls at the application layer to complete its assigned task.

“AI’s extraordinary potential for society will only be realized if we solve AI safety,” said Jensen Huang, founder and CEO of NVIDIA. “As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety. Safety and security require full-stack engineering. NVIDIA Open Agent Safety Platform brings together industry, researchers and public-sector organizations to share best practices, align on evaluation methods and foster international cooperation. Together, we can raise the bar for global AI safety.”

Open Agent Safety Platform Adds Control Across the Full Agent Stack
NVIDIA Open Agent Safety Platform enables full-stack governance and control across the software that runs agents, the hardware and compute layers that power their work, and the robotics systems that execute tasks in the physical world. Organizations can deploy elements of NVIDIA Open Agent Safety Platform according to their unique requirements.

It includes NVIDIA OpenShell™ secure runtime software that sets boundaries for agents running on CPUs. As agents take on more work across more systems, enterprises need an enforceable boundary outside of the model and agent harness. Now broadly available, OpenShell provides a secure runtime boundary for controlling how autonomous AI agents execute tasks across open and closed models.

OpenShell delivers this protection with minimal overhead on NVIDIA Vera, the first purpose-built CPU for agentic AI. Together, OpenShell and Vera enable agents to operate securely while completing their work as quickly as possible. As open source software, OpenShell can also be extended to work with third-party compute platforms, including those from Arm and Intel.

The NVIDIA Open Agent Safety Platform reference system design features NVIDIA Sentry, an out-of-band watchdog that runs on NVIDIA BlueField^®-4 DPUs to continuously monitor agent behavior. Sentry provides in-silicon security enforcement, meaning that if an AI agent attempts to move outside its software boundary, Sentry quarantines and stops it in milliseconds.

Running on BlueField-4 DPUs, Sentry continuously monitors agent activity and enforces security policies independently in silicon. It combines threat detection, hardware-based agent governance and enforcement and data access protection from an isolated, out-of-band trust domain that is responsive in real time and invisible to agents and attackers.

Sentry is built on NVIDIA DOCA™ software, which provides the programmable capabilities Sentry uses to inspect agent requests and responses, provide attested telemetry, verify agent identity and enforce granular, zero-trust access policies for data, tools, application programming interfaces and services.

Industry Leaders Strengthen Agent Security With NVIDIA
Anthropic and NVIDIA have collaborated to bring additional layers of security and control to the agent stack. Claude Managed Agents establish a security boundary by running the agent loop in a separate server from the sandboxes where their work executes. Integrations with OpenShell and BlueField enable enterprises to enforce strict control over agent access through those sandboxes.

“Companies are giving AI agents more of their most important work, and they need to direct and verify what those agents do, especially in sensitive environments,” said Paul Smith, chief commercial officer of Anthropic. “Claude Managed Agents gives companies a clear view of what each agent is doing, and NVIDIA’s platform adds another layer of governance and control across hardware and software.”

SpaceXAI is using NVIDIA Open Agent Safety Platform for Cursor coding agents and Grok models.

“As customers rely more on agents to get real work done, safety should be enforced outside the model by additional controls the agent can’t get past,” said Mike Nicolls, president at SpaceXAI. “Customers should be able to set those limits for Cursor and Grok and trust they will hold.”

Scale AI is working with NVIDIA to incorporate NVIDIA Open Agent Safety Platform technologies into the agentic infrastructure layer of Scale GenAI Portfolio.

“Scale AI is using the NVIDIA Open Agent Safety Platform reference design to build reliable agentic AI systems for our enterprise and government customers running mission-critical applications, with isolation, policy enforcement and auditability built in from the start,” said Francis deSouza, CEO of Scale AI. “We support agentic security with clear boundaries that define what agents can do, and controls that keep them operating within those permissions.”

Salesforce and NVIDIA have integrated OpenShell with Slack, enabling teams to manage OpenShell agent activity directly from Slack — viewing agent activity and audit events, and approving or rejecting agent requests for additional permissions — giving teams greater visibility and human oversight as agents work.

SAP is embedding OpenShell with Joule Studio runtime, part of the SAP Business AI Platform, to pair business oversight with runtime security. The company is also contributing engineering work to OpenShell and working with NVIDIA to advance interoperability standards through the Open Secure AI Alliance.

Accenture, Armadin, Cadence, Cognition, CrowdStrike, Cisco, Dassault Systèmes, Deloitte, EY, Hugging Face, IBM, Irregular, Perplexity, Microsoft, SAP, Scale AI, ServiceNow, Siemens, Synopsys, OpenClaw, Palantir and Palo Alto Networks are also among the over 100 organizations working with NVIDIA Open Agent Safety Platform technologies.

Robotics leaders — such as Figure, Gecko Robotics and Skild AI — are also building with OpenShell to embed agent safety controls into autonomous systems that take action in the physical world.

Citi and JPMorganChase are among the financial services leaders collaborating with NVIDIA on shared open source agent safety technologies.

Energy leaders Hitachi Energy, EPRI, NextEra Energy, Quanta Services, SPP, Schneider Electric, Siemens Energy and Worley are among critical U.S. infrastructure providers working with NVIDIA Open Agent Safety Platform technologies.

Infrastructure software leaders Canonical, SUSE and Red Hat are also integrating NVIDIA Open Agent Safety Platform into widely used software operating systems. Red Hat runs OpenShell and DOCA, both part of NVIDIA Open Agent Safety Platform, on Red Hat AI Factory with NVIDIA, a co-engineered, enterprise-grade AI solution for building, deploying and managing AI at scale across hybrid cloud environments.

NVIDIA partners including Baseten, Cisco, CoreWeave, Dell Technologies, GMI Cloud, HPE, HP Inc., Irregular, Lenovo, Microsoft, Nebius, Oracle Cloud Infrastructure, Supermicro and Together AI are among those offering AI infrastructure solutions that use and support NVIDIA Open Agent Safety Platform technologies to help customers run AI agents more securely.

Availability
NVIDIA Open Agent Safety Platform software, including OpenShell and skills, are available through the NVIDIA developer resources page and GitHub.

Ecosystem contributions such as NVIDIA Open Agent Safety Platform support the mission of the Open Secure AI Alliance as well as the broader AI safety and security community. Initiated by NVIDIA alongside over 120 leading organizations and governed by the Linux Foundation, the Open Secure AI Alliance strengthens AI agent security through open research, skills and tools, as well as projects like the Shared AI Findings Exchange, or SAFE.

The post NVIDIA Launches Open Agent Safety Platform to Secure Agents From Testing to Deployment appeared first on Tech Scene ZA.

News Summary:

• The NVIDIA CUDA-Q platform now includes the CUDA-Q Logical orchestration layer, giving the quantum industry an open, programmable way to design and test fault-tolerant quantum computing applications in areas such as drug discovery, financial modeling and materials development.
• Fermilab used CUDA-Q Logical to accelerate fault-tolerant architecture development from five months to three weeks — a 7x speedup.
• QUOPS is a benchmark for the future of quantum computing, measuring the readiness of fault-tolerant quantum hardware for practical applications. Developed by Sandia National Laboratories, QUOPS is now available in NVIDIA CUDA-Q.

NVIDIA today announced an expansion of the NVIDIA CUDA-Q™ open source platform with CUDA-Q Logical, an orchestration layer that provides a programmable, verifiable approach to developing useful applications for fault-tolerant quantum computers.

Fault-tolerant quantum processors with logical qubits are essential for useful quantum computing, allowing systems to overcome the errors inherent in physical qubits so they can execute the larger computations needed for practical applications such as drug discovery, financial modeling and materials development.

However, developing applications for such systems is a tedious, time-consuming codesign challenge: A change to an algorithm, error-correction code, hardware architecture or other QPU component can significantly change the resources needed to run any given application.

With CUDA-Q Logical, researchers can now design and orchestrate the many components required for fault-tolerant quantum computing applications, easily switching between options to identify optimal system configurations for performance with logical qubits.

“Quantum computing is maturing into an era of logical qubits, and researchers need an open, customizable platform capable of representing all aspects of a fault-tolerant system,” said Timothy Costa, vice president and general manager of quantum at NVIDIA. “The addition of CUDA-Q Logical provides power and flexibility to explore fully integrated, co-optimized systems regardless of qubit type and architecture — drastically shortening the timeline to useful quantum-GPU supercomputing.”

CUDA-Q Logical is already being used by QPU makers and labs including Fermi National Accelerator Laboratory, Infleqtion, IQM Quantum Computers, QCDesign Quantum Motion and Sandia National Laboratories.

Using CUDA-Q Logical, Iceberg Quantum modeled its fault-tolerant architecture for Diraq’s qubits, showing how 1,000 logical qubits can be created with just 150,000 physical qubits, roughly 10x fewer than Diraq’s previous estimates. CUDA-Q Logical gave an unprecedented way to rapidly assess potential implementations of Iceberg’s architecture, helping dramatically reduce the hardware needed for useful, fault-tolerant quantum computing.

Fermilab Uses CUDA-Q Logical to Explore Fault-Tolerant Applications
In early work, Fermilab researchers used CUDA-Q Logical to validate prior results and evaluate physical qubits, runtimes and other resource requirements across different error-correction approaches and quantum hardware.

Fermilab transformed complicated fault-tolerant system designs into a repeatable and verifiable computational workflow, accelerating fault-tolerant algorithm development from five months to three weeks, a 7x speedup.

“Fault-tolerant quantum computing is the path to unlocking new scientific discovery, but getting there will require researchers to codesign algorithm, error correction, architectures and hardware together,” said Anna Grassellino, chief technology officer at Fermilab and director of the Superconducting Quantum Materials and Systems Center. “Using CUDA-Q Logical, our team explored combinations of these resources in just three weeks, compared with what would have typically taken about five months of building specialized infrastructure.”

Sandia Creates Benchmark for Future of Quantum Computing
QUOPS is a new, independent cross-platform benchmark developed by Sandia National Laboratories that measures the progress quantum computing systems are making toward utility-scale applications.

With Sandia’s QUOPS, the industry now has access to a hardware-agnostic, open method to benchmark progress toward shared goals for fault-tolerant quantum computers. Historically, progress in quantum computing has been measured primarily through advances in physical qubits — increasing qubit counts, improving fidelity and extending coherence.

“At Sandia, we can’t wait to see fault-tolerant quantum computers helping to solve problems of national importance for the Department of Energy and the United States,” said Timothy Proctor, co-director of Sandia National Laboratories’ Quantum Performance Laboratory. “Our mission right now is to bring that about sooner, by accelerating our industry partners’ progress. To do that, we, and other quantum computing stakeholders, have to be able to track and forecast the growth of quantum computer abilities. We created QUOPS to do exactly that, and we’re excited to see it used by quantum computing vendors and customers.”

Sandia shared early results in a preprint posted ahead of IEEE Quantum Week, reporting initial QUOPS benchmarks for QPUs from Google, IBM and Quantinuum. A QUOPS reference implementation is available in NVIDIA CUDA-Q.

Expanding the Quantum-GPU Supercomputing Platform
The ecosystem is also adopting NVIDIA’s larger lineup of quantum computing technologies for turning qubits into useful quantum-GPU supercomputers.

Diraq used NVIDIA Ising, the world’s first family of open models for building and deploying AI for useful quantum computing, to calibrate its silicon-based qubit processor.

Companies are integrating their quantum processors with GPU supercomputing using NVIDIA NVQLink™, the open system architecture for tightly coupling quantum processors with GPU supercomputers. With NVQLink:

• Anyon Computing developed a new quantum control system.
• Quandela developed a QPU-GPU architecture.
• Quantum Machines ran a demo of integrating state-of-the-art supercomputing resources with qubits at the Israeli Quantum Computing Center.

The NVIDIA CUDA-Q open development platform continues to be adopted across the quantum computing ecosystem:

• BlueQubit launched the Quantum Flywheel grant program for researchers, to give them access to NVIDIA accelerated computing through CUDA-Q.
• Qedma Quantum Computing and QCentroid each integrated their technology with CUDA-Q to support quantum error correction and mitigation, as well as the deployment of quantum applications.

The quantum ecosystem is also working with NVIDIA to develop new and improved quantum applications:

• IonQ announced progress on DQAOA-GPT, a quantum generative AI framework, using NVIDIA accelerated computing.
• MITRE published work on building GPU-accelerated digital twins of quantum sensors.
• Phasecraft is using NVIDIA cuQuantum to generate the largest-known variational quantum eigensolver emulated molecular database.
• UCLA and Caltech are advancing the design of control sequences needed to run quantum applications.

Availability
CUDA-Q Logical is now available through GitHub. Explore the QUOPS repository.

Learn more by reading these papers on CUDA-Q Logical and QUOPS.

The post NVIDIA Expands Open Source CUDA-Q Platform for Fault-Tolerant Quantum Computing appeared first on Tech Scene ZA.

News Summary:

• SpaceXAI will deploy NVIDIA Vera CPUs to accelerate the work behind its next generation of agentic AI workloads.
• SpaceXAI is expanding its AI infrastructure for Grok with the NVIDIA Vera Rubin platform as it scales toward gigawatts of computing capacity.
• SpaceXAI plans to extend its use of NVIDIA accelerated computing into space, with the first-generation Starmind AI satellite based on an optimized NVIDIA Vera Rubin NVL72 system.

NVIDIA today announced that SpaceXAI will deploy NVIDIA Vera CPUs to accelerate its next generation of agentic AI applications, bringing the first CPU built for AI agents to one of the world’s most ambitious AI deployments.

Agentic AI applications increasingly rely on CPUs to orchestrate tools, execute code, process data and run simulations between model calls. SpaceXAI will use Vera to accelerate these application workloads, helping AI agents act faster while keeping GPUs fed and fully utilized.

SpaceXAI plans to expand its AI infrastructure behind Grok on NVIDIA Vera Rubin, while extending an optimized Vera Rubin NVL72 into space with its first-generation Starmind satellite.

“Agentic AI requires a new kind of computing system — one built not only to generate answers, but to take action,” said Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA. “Vera gives AI agents the CPU performance to act in real time — executing code, processing data and coordinating complex tasks. SpaceXAI is taking this architecture from massive AI factories to the next frontier of computing in orbit.”

“Vera gives us the CPU performance and memory bandwidth to run enormous amounts of orchestration, code and data processing while keeping GPUs doing what they do best,” said Mike Nicolls, president of SpaceXAI. “That means higher-performance AI agents and more useful work from every watt of compute.”

NVIDIA Vera — The CPU for Agents
NVIDIA Vera is the first CPU built for AI agents, designed to accelerate the CPU-intensive work that surrounds model inference — from tool use and code execution to data processing, orchestration and simulation.

Vera features 88 NVIDIA-designed Olympus cores, NVIDIA Spatial Multithreading technology and high-bandwidth LPDDR5X memory, delivering up to 1.2TB/s of bandwidth. Vera enables up to 1.8x faster task completion compared with x86 CPUs across workloads including agentic AI, reinforcement learning and data processing.

SpaceXAI Scales AI Infrastructure With NVIDIA Vera Rubin
At massive scale, AI infrastructure must support demanding training, reasoning and inference workloads while maximizing performance, power efficiency and utilization. NVIDIA Vera Rubin is codesigned across compute, networking and software to optimize the AI factory as a whole.

The platform brings together NVIDIA accelerated computing, NVIDIA NVLink™ interconnect technology, NVIDIA Spectrum-X™ Ethernet networking, NVIDIA BlueField^® data processing and NVIDIA software in an integrated architecture designed to deliver high performance and energy efficiency at scale while driving down cost per token.

As SpaceXAI expands toward gigawatts of computing capacity, Vera Rubin provides a common architecture to efficiently scale its next generation of AI factories — a foundation SpaceXAI plans to take beyond terrestrial data centers and into orbital computing.

NVIDIA Accelerated Computing, From Earth to Orbit
SpaceXAI’s work with NVIDIA is moving beyond AI factories on Earth.

SpaceXAI is developing AI computing infrastructure for orbit, where power, thermal management, bandwidth, reliability and physical integration impose dramatically different constraints from conventional data centers.

SpaceXAI’s planned first-generation Starmind AI satellite will be based on the optimized NVIDIA Vera Rubin NVL72 rack-scale system, extending the same accelerated computing architecture powering next-generation AI factories on Earth into space.

NVIDIA and SpaceXAI are working to adapt that foundation to the requirements of orbital computing while preserving a common NVIDIA architecture and software ecosystem.

This delivers one computing foundation across a wide range of environments: Vera CPUs accelerating increasingly sophisticated AI agents, Vera Rubin powering the AI infrastructure behind Grok and gigawatt-scale AI factories on Earth, and NVIDIA accelerated computing extending into orbital AI infrastructure.

Learn more about the NVIDIA Vera Rubin platform and NVIDIA Vera CPU.

The post SpaceXAI Adopts NVIDIA Vera CPU to Accelerate Agentic AI at Massive Scale appeared first on Tech Scene ZA.

Groq 3 LPX Extends the Vera Rubin Platform, Delivering Ultrafast Token Generation for the Next Generation of Agentic AI, With Nebius the First to Adopt

News Summary:

• In Artificial Analysis benchmarking, Groq 3 LPX showcased world-class speed for agentic coding and other latency-sensitive workloads.
• NVIDIA Groq 3 LPX extends the inference performance of NVIDIA Vera Rubin NVL72 systems by dramatically increasing token generation rates.
• Nebius is the first AI cloud to adopt NVIDIA Groq 3 LPX.

Hot Chips—NVIDIA today announced that NVIDIA Groq 3 LPX, the interactive AI inference accelerator, is now in full production. An extension of the NVIDIA Vera Rubin platform, Groq 3 LPX delivers a major boost in AI inference by enabling ultrafast token generation for highly responsive agentic systems.

Agentic systems can generate massive volumes of tokens across hundreds or thousands of inference steps, making faster token generation critical for agents to reason, act and complete complex tasks in real time.

Vera Rubin NVL72 systems provide the most versatile training and inference platform for every AI factory. NVIDIA Groq 3 LPX extends the inference performance of Vera Rubin NVL72 by dramatically increasing the rate of token generation, providing premium user experiences for context-heavy workloads so agents can act at extreme speeds.

NVIDIA Groq 3 LPX is pushing the frontier of AI inference. It delivered a record 3,400 output tokens per second in Artificial Analysis benchmarking running Gemma 4 31B, an open source agentic model, with a 100,000-token context critical for agentic systems — the fastest performance ever recorded for the model.

Groq 3 LPX enables agentic tasks such as coding in minutes versus hours, providing 4x faster responsiveness for agents and latency-sensitive workloads than the nearest alternative platform.

“Inference is the growth engine of AI. NVIDIA Grace Blackwell and NVL72 revolutionized large language model inference with an unprecedented leap in performance and efficiency,” said Jensen Huang, founder and CEO of NVIDIA. “Vera Rubin extends that vision with workload-optimized AI factory configurations designed for the era of agentic AI, advancing the performance frontier with LPX for ultrafast token generation. This transforms how intelligence is produced, delivering another giant leap in AI throughput, efficiency and responsiveness, just as demand for AI computation is accelerating worldwide.”

Groq 3 LPX — The Interactive AI Inference Accelerator
Agentic AI creates two distinct computing challenges: efficiently processing enormous amounts of context and generating tokens with extremely low latency.

NVIDIA Groq 3 LPX is purpose-built to extend Vera Rubin’s interactivity — the rate at which tokens are generated for an individual user, determining how quickly an agent can complete each step of its work.

Faster generation gives agents more time to inspect files, write and test code, call tools, verify results and iterate while maintaining a responsive user experience.

AI Cloud Momentum for Groq 3 LPX
AI clouds are becoming the engines of the AI economy, giving enterprises and developers access to advanced infrastructure for training, reasoning and inference at scale. For providers serving latency-sensitive, high-volume inference workloads, NVIDIA Groq 3 LPX provides a path to deploy differentiated compute in proven rack-scale systems.

Nebius, a leading AI cloud, plans to bring NVIDIA Groq 3 LPX to Nebius Token Factory, its production inference platform, giving developers access to extreme token generation speed for highly responsive agentic AI applications.

“Generation is the phase of inference that determines how responsive an AI system actually is, and that’s exactly what NVIDIA Groq 3 LPX is built to accelerate,” said Danila Shtan, chief technology officer of Nebius. “As the first AI cloud bringing it to production via Nebius Token Factory, we’re making sure every step of an agent’s loop feels instant — through the same API developers are already using, with no migration to a new stack.”

Following Nebius, purpose-built AI inference cloud Groq plans to be among the platform’s earliest adopters.

Extreme Codesign for AI Factories
Through extreme codesign across seven chips and five purpose-built racks, NVIDIA Vera Rubin is the most extensive AI factory platform.

NVIDIA Vera Rubin NVL72 and Groq 3 LPX tackle the various workload requirements of customer AI factories, including frontier model makers and open model service providers.

These rack platforms feature NVIDIA BlueField®-4 DPUs and work in combination with NVIDIA Vera CPU racks, NVIDIA Vera BlueField-4 STX storage and NVIDIA Spectrum™-6 SPX Ethernet to optimize multi-agent systems for the highest throughput per watt and the lowest-latency inference.

The post NVIDIA Groq 3 LPX Now in Full Production With World-Class Speed for Agentic AI appeared first on Tech Scene ZA.

Companies Deepen Integration Across the AI Stack, Bringing NVIDIA Vera CPUs, Advanced Networking, Nemotron Open Models and Physical AI Technologies to AWS as Customer Demand Accelerates

August 26, 2026

Amazon Web Services (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced a major expansion of their strategic collaboration to meet surging global demand for AI infrastructure as demand continues to accelerate. Building on already-rapid customer adoption of NVIDIA-accelerated compute on AWS, the companies plan to deploy 2 million additional NVIDIA GPUs across AWS’s global infrastructure and deepen their work together across AI factories, CPUs, networking, open models, data processing and robotics, delivering co-engineered AI solutions that enable customers to accelerate AI development and deployment at unprecedented scale.

AI workloads are scaling at a swift pace, from how models are trained and run, to how data is processed, indexed and used to power intelligent applications. Customers are moving from pilot to production and scaling workloads across agentic AI, scientific discovery, enterprise automation and robotics. They need broader model choice, faster data pipelines and new capabilities for emerging use cases like physical AI. They also need confidence that the underlying infrastructure can keep pace with their own ability to innovate while maintaining the highest level of security and reliability for mission-critical workloads.

To meet this surging demand from frontier labs, global enterprises, startups and governments, AWS and NVIDIA are building on 16 years of joint innovation to expand AI compute capacity and bring new co-engineered solutions to customers faster. As part of the expanded collaboration, the companies are working to:

• Deploy 2 million additional NVIDIA GPUs across AWS’s global infrastructure in 2027-2028
• Bring NVIDIA Vera CPU‑based infrastructure to AWS
• Extend NVIDIA NVLink Fusion™ with custom NVIDIA high‑bandwidth memory (NVHBM)
• Build AI factories for the U.S. government, including 100,000 GPUs on secure AWS infrastructure for running federal and national‑security workloads
• Integrate the NVIDIA platform with the AWS Nitro System and Elastic Fabric Adapter (EFA) for enhanced security and reliability
• Continue to support NVIDIA Nemotron™ open models on Amazon Bedrock and Amazon SageMaker, giving customers more open model choice
• Accelerate data processing and vector indexing on Amazon EMR and Amazon OpenSearch with NVIDIA cuDF and cuVS CUDA-X™ libraries for faster, more cost‑efficient analytics and AI applications
• Further advance robotics workloads through Amazon Robotics’ adoption of NVIDIA’s physical AI platform, speeding innovation in warehouse automation and next‑generation robots

“Customers want the freedom to choose the best tools for their AI workloads, and they want confidence that everything works seamlessly together,” said Matt Garman, CEO of AWS. “That’s why we’ve invested deeply with NVIDIA to make AWS the best place to run NVIDIA AI technologies, optimizing performance across our infrastructure from networking and security to deployment. This expanded collaboration gives frontier labs, enterprises and governments even more ways to build and deploy AI on AWS.”

“NVIDIA and AWS have built one of the great growth engines of the AI era, and demand is running ahead of every forecast,” said Jensen Huang, founder and CEO of NVIDIA. “For 16 years, we have scaled NVIDIA computing in the cloud together. Now, we are expanding our partnership across the full stack — GPUs, CPUs, networking, open models and software — to make agentic and physical AI real at an unprecedented pace and scale that only AWS and NVIDIA can deliver. This expansion reflects customers’ demand for NVIDIA’s platform on AWS.”

Massive Expansion of AI Compute Capacity
AWS offers the widest range of GPU-based instances of any cloud provider to power a diverse set of AI and machine learning workloads. At NVIDIA GTC 2026, AWS announced plans to add more than 1 million NVIDIA GPUs starting in 2026. Since then, demand has exceeded those expectations. AWS plans to deploy an additional 2 million NVIDIA Blackwell Ultra, Rubin and Rubin Ultra GPUs in 2027-2028 across AWS Global Infrastructure, including AI factories. This additional capacity will help power customer workloads ranging from agentic AI and scientific discovery to enterprise automation and physical AI. In addition, AWS will expand NVIDIA Blackwell capacity, including NVIDIA RTX PRO™ 4500 Blackwell Server Edition GPUs for Amazon EC2 G7 instances. G7 instances deliver 4.6x AI inference performance and 2.1x graphics performance compared to previous-generation G6 instances. AWS is the first major cloud provider to offer compute instances accelerated by RTX PRO 4500. AWS and NVIDIA are also collaborating on NVIDIA Spectrum™ networking to further optimize network performance for large-scale AI training workloads across GPU clusters.

Support for NVIDIA Vera CPUs on AWS
AWS and NVIDIA are working to bring Vera CPU-based infrastructure to AWS, providing an additional option to support agentic AI workloads that require high-performance CPU compute alongside accelerated infrastructure. Purpose-built for the next generation of AI, Vera complements AWS’s strategy to offer the broadest choice of compute — from AWS custom silicon to the latest accelerators and CPUs from partners.

Heterogeneous AI Infrastructure Using NVIDIA NVLink Fusion With NVHBM
At re:Invent 2025, AWS announced support for NVIDIA NVLink Fusion high-speed chip interconnect technology in next-generation Trainium chips. NVIDIA and Amazon’s Annapurna Labs are expanding that support to work on NVIDIA’s new custom high-bandwidth memory (NVHBM) technology, in partnership with memory suppliers, which would give Trainium access to faster, more power-efficient memory. Combined with NVLink Fusion, Annapurna Labs can now tap NVIDIA’s custom memory technology and scale-up architecture to enhance performance and efficiency for AI workloads while seamlessly integrating Trainium and GPUs within a common rack-scale architecture.

Powering Federal AI at the Highest Levels of Security
Government agencies need secure AI infrastructure to keep pace with the demands of national security. AWS and NVIDIA plan to build AI factories for the U.S. government, delivering NVIDIA’s AI stack, including plans to deliver 100,000 GPUs on AWS’s secure infrastructure for federal and national-security workloads. This collaboration puts AWS and NVIDIA at the center of federal AI advancement for national security, enabling government agencies to deploy AI at scale for workloads classified at Impact Level 6 (IL6) and above.

These new commitments build on a foundation of deep technical integrations between AWS and NVIDIA that are already delivering results for customers today, including:

Physical AI for robotics — Amazon Robotics is collaborating with NVIDIA to accelerate the development of next-generation robots integrating NVIDIA’s full-stack physical AI platform, including the NVIDIA Jetson™ platform, NVIDIA Omniverse™ libraries and the NVIDIA Isaac™ open robotics development platform. The collaboration spans simulation, synthetic data generation, robot training, route optimization, functional safety and real-to-sim validation — all running on GPU-accelerated Amazon EC2 instances. Together, AWS and NVIDIA are helping advance the capabilities that robotics workloads require at scale: massive simulation, diverse training data and continuous real-world validation.

Enhanced security and reliability with AWS Nitro System and EFA — Across this expanded collaboration, all NVIDIA GPU-based and Trainium-based EC2 instances — including those leveraging NVLink Fusion — are built on the AWS Nitro System and interconnected through EFA. Both GPU-accelerated and Trainium-based EC2 instances will continue to be built on the Nitro System and scaled out through EFA. Together, Nitro and EFA help ensure that as AWS expands its NVIDIA GPU fleet and integrates new interconnect technologies, customers retain the security, reliability and network performance they depend on for production AI workloads at scale.

NVIDIA Nemotron models on AWS — As part of AWS’s commitment to offering customers the broadest choice of AI models, NVIDIA’s Nemotron family of open models is available on Amazon Bedrock as fully managed, serverless models and on Amazon SageMaker for customers who want to deploy and fine-tune on their own infrastructure. This integration gives customers access to NVIDIA’s latest open models with the security, scalability and operational tooling of AWS.

GPU-accelerated data processing and vector indexing — As data volumes grow, workloads such as feature engineering, large-scale ETL and real-time analytics require increasingly faster processing. AWS and NVIDIA are collaborating to deliver GPU-accelerated data processing on Amazon EMR using Amazon EC2 G7 instances and the NVIDIA cuDF library, delivering up to 3.7x faster processing speeds and a 30% better price performance compared to CPU-based configurations. Separately, as AI applications, retrieval-augmented generation pipelines and semantic search push vector databases to billions of records, index building and tuning becomes a bottleneck. GPU-accelerated vector indexing on Amazon OpenSearch Service offloads index construction onto dedicated GPUs, delivering up to 9x faster vector indexing at a quarter of the cost — available across both managed clusters and Amazon OpenSearch Serverless.

The post AWS and NVIDIA to Deliver 2 Million Additional GPUs and Next-Generation Infrastructure for Agentic and Physical AI appeared first on Tech Scene ZA.

News Summary:

• NVIDIA Jetson Orin Nano 2, a new robotics computer for entry-level edge AI, enables millions of developers worldwide to build robots, delivery and inspection drones, and vision AI systems for frontier physical AI applications.
• Jetson Orin Nano 2 delivers 2x the inference performance of its predecessor in the same form factor and consumes 40% less power at the same performance.
• More than 3 million developers have built on the NVIDIA robotics stack, with Cognex, Doosan Bobcat and Matic among the first to adopt and explore Jetson Orin Nano 2.
• Ecosystem partners are building carrier boards, hardware systems and reference solutions to help customers accelerate time to market.

NVIDIA today announced NVIDIA Jetson Orin Nano™ 2, a new robotics computer set to redefine entry-level edge AI — putting frontier-class generative AI performance in the hands of millions of developers.

As AI models grow smaller and more efficient, more edge devices can become autonomous systems that understand context, interpret language and images, and act in real time. To bring these capabilities into physical machines like robots, delivery and inspection drones, and vision AI systems, developers need compact, energy-efficient robotics computers built for edge AI.

“Today’s small and medium frontier models have reached the accuracy of last year’s largest frontier models, unlocking real-time intelligence for edge devices,” said Deepu Talla, vice president of robotics and edge AI at NVIDIA. “The Jetson Orin Nano 2 computer puts that breakthrough within reach of millions of developers, delivering the performance and energy efficiency needed for real-time reasoning at the edge.”

Cognex, Doosan Bobcat and Matic are among the first to adopt and explore Jetson Orin Nano 2.

A Major Leap for Entry-Level Edge AI
Featuring 78 trillion operations per second of AI compute, 8GB of memory and an 8-core Arm CPU, Jetson Orin Nano 2 delivers a significant leap in AI and video-processing performance in a cost-effective, power-efficient system.

Jetson Orin Nano 2 achieves 2x the inference performance of Jetson Orin Nano Super through improved Tensor Cores and higher memory bandwidth, while maintaining the same compact form factor. In 15-watt mode, Jetson Orin Nano 2 consumes 40% less power to deliver the same performance as its predecessor.

Powered by NVIDIA’s open software stack, NVIDIA Jetson™ agent skills and a rich AI ecosystem, Jetson Orin Nano 2 enables developers to run the latest large language models and vision language models optimized for memory-efficient edge inference — including open models like NVIDIA Cosmos™ and NVIDIA Nemotron™, Gemma 4 and Qwen 3 — to rapidly build advanced AI applications.

Partner Ecosystem and Millions of Robotics Developers Build on Jetson
More than 3 million developers are building on NVIDIA’s robotics stack, and with Jetson Orin Nano 2, partners like Cognex, Doosan Bobcat, Matic and Wing are bringing real-world edge AI applications to compact devices, including home robots, vision AI systems, delivery and inspection drones, carrier boards, hardware systems and reference solutions.

Wing, a leading drone delivery company and subsidiary of Alphabet, is using Jetson Orin Nano Super and the NVIDIA software stack in its delivery drone fleet — and plans to evaluate Jetson Orin Nano 2 to advance real-time AI perception and reasoning for faster, safer delivery from local businesses to residential yards.

“Drone delivery depends on AI that can enable fast, reliable understanding of the real world,” said Dinuka Abeywardena, head of perception at Wing. “Wing is exploring Jetson Orin Nano 2 to give us a path to more responsive, energy-efficient drones that can help make deliveries quicker and more dependable for customers.”

Matic Robots, an advanced consumer robotics company, is adopting Jetson Orin Nano 2 to enable more intuitive interactions with its home cleaning robots, enabling them with conversational AI, gesture detection, deeper context of the home with precision mapping and semantic understanding, and autonomous cleaning capabilities.

“Home robots need to understand people, map spaces precisely, understand the layout of objects and spaces, and clean autonomously in dynamic and constantly changing environments,” said Navneet Dalal, cofounder and CEO of Matic Robotics. “With Jetson Orin Nano 2, Matic can run state-of-the-art AI models at the edge in a compact home robotics platform built for real-time perception, interaction and navigation.”

AAEON, ADLINK, Advantech, Aetina, Antmicro, Aptiv, Auvidea, AVerMedia, Chuanglebo, Connect Tech, ForeCR, JWIPC, Neurealm, Plink, Realtimes, RidgeRun, RS, Seeed Studio, Tauro Tech, Twowin, TZTEK and YUAN are among other Jetson partners building carrier boards, hardware systems, customized AI software and reference solutions to help customers accelerate time to market.

Availability
The NVIDIA Jetson Orin Nano 2 module and developer kit are expected to be available in the first half of 2027.

The post NVIDIA Announces Jetson Orin Nano 2 Robotics Computer to Redefine Entry-Level Edge AI appeared first on Tech Scene ZA.

Collaboration Will Provide World-Class AI Infrastructure, Research Funding, Internships and Joint Appointments to Advance Agentic AI Model Development for Korea

News Summary:

• NVIDIA and KAIST are launching a joint AI research lab at the KAIST Kim Jaechul Graduate School of AI in Seoul, dedicated to advancing agentic AI models and agent systems built for South Korea’s industries, language and future.
• The collaboration includes compute contributions, funding for at least 10 KAIST researchers annually with NVIDIA internships, plus full-time NVIDIA roles for top Korean researchers — creating new pathways for Korea’s AI talent.
• A core focus is developing models optimized for Korea, using NVIDIA Nemotron open models and local NVIDIA Cloud Partner infrastructure to build a pipeline from academic research to enterprise and national AI deployments.
   

NVIDIA and the Korea Advanced Institute of Science and Technology (KAIST) today announced the launch of a joint AI research laboratory at the KAIST Kim Jaechul Graduate School of AI in Seoul, dedicated to advancing agentic AI for South Korea.

The collaboration will establish a robust academic AI research program, bringing together NVIDIA full-stack AI expertise, NVIDIA Nemotron™ open models and NVIDIA AI Cloud partner computing with the world-class scientific talent at KAIST, one of Asia’s premier research universities.

“Korea is home to leading AI researchers and is one of the world’s most advanced technology ecosystems,” said Bill Dally, chief scientist and senior vice president of research at NVIDIA. “The joint NVIDIA-KAIST research lab will provide a foundation for the next frontier of AI research to accelerate AI models and agent systems built for Korea’s industries, language and future.”

“AI research is entering a new era — one that requires frontier talent, large-scale infrastructure and deep collaboration across academia and industry,” said Hyunwoo Kim, incoming faculty member at the KAIST Kim Jaechul Graduate School of AI, who will serve as head of the joint NVIDIA-KAIST lab upon joining KAIST. “Together, NVIDIA and KAIST Kim Jaechul Graduate School of AI will pursue ambitious work that helps Korea attract and retain top AI scientists while building lasting ties with NVIDIA’s global research organization.”

Full-Stack Infrastructure, Open Models and Collaboration Fuel Korea’s AI Future
The lab will be established at the KAIST Kim Jaechul Graduate School of AI in Seoul. KAIST, headquartered in the tech hub of Daejeon, has a strong focus on public research spanning engineering, AI, semiconductor technology, robotics and digital humanities.

The joint lab plans to fund at least 10 KAIST researchers annually and provide each with internship opportunities at NVIDIA. In addition, NVIDIA plans to hire exceptional Korean researchers for full-time positions. Together, these efforts will create stronger pathways for Korea’s top AI talent to pursue ambitious research, build long-term careers and deepen global collaboration between academia and industry.

The $300 million collaboration is expected to include $50-million-per-year compute contributions across an initial five-year period. Compute infrastructure from local NVIDIA Cloud Partners will provide researchers with direct access to the latest NVIDIA AI infrastructure.

Among the lab’s priorities will be developing models optimized for the Korean language and Korea-specific use cases, with NVIDIA Nemotron open models to advance the country’s AI capabilities, fostering a pipeline from academic discovery to enterprise and national AI deployments.

The post NVIDIA and KAIST Launch Joint AI Research Lab to Accelerate AI Innovation in Korea appeared first on Tech Scene ZA.

News Summary:

• NVIDIA Agent Toolkit now includes NVIDIA Omniverse libraries, giving AI agents tools and skills to help software developers integrate physical AI capabilities into their existing applications.
• New Omniverse libraries for NVIDIA RTX sensor simulation, GPU-accelerated physics simulation and simulation-ready asset validation are openly available on GitHub.
• SideFX and PTC are integrating Omniverse libraries into 3D applications for physical AI, with support for cloud and local AI systems, from NVIDIA RTX Spark to NVIDIA DGX Station.
• New NVIDIA blueprint for integrating Omniverse libraries in Blender.

NVIDIA today announced that NVIDIA Agent Toolkit now includes NVIDIA Omniverse™ libraries — a collection of software components that give AI agents tools and skills to add physical AI capabilities to existing applications and prepare 3D content for simulation.

Robots, factories and autonomous systems need to be designed, tested and trained in simulation before they operate in the real world. Preparing 3D content for simulation takes more than realistic visuals — assets need the right structure, materials, scale, labels, sensors and physical properties. With NVIDIA Omniverse libraries in NVIDIA Agent Toolkit, AI agents have the tools and skills to build workflows, inspect scenes, flag issues and prepare assets, helping developers move faster from 3D content to simulation-ready environments.

“The physical AI era will be built in simulation first,” said Jensen Huang, founder and CEO of NVIDIA. “NVIDIA Agent Toolkit with Omniverse libraries brings AI agents into the 3D tools developers already use, helping build the simulation-ready worlds where robots, factories and autonomous systems are trained and tested long before they reach the real world.”

Software makers including SideFX and PTC are integrating Omniverse libraries for agent-ready sensor simulation, physics and asset validation, helping bring agentic AI into the applications and workflows developers and technical artists already use to prepare 3D content.

Omniverse Libraries Bring Physical AI Skills to NVIDIA Agent Toolkit
NVIDIA Agent Toolkit helps software makers build AI agents that connect tools, skills and data sources. Omniverse libraries extend those agents into 3D and physical AI workflows with callable tools for sensor simulation, GPU-accelerated physics and simulation-ready asset validation inside existing applications.

The new Omniverse libraries — including ovrtx, ovphysx and CAD-to-SimReady skills — are openly available on GitHub, giving AI agents tools to build workflows for inspecting scenes, testing changes and preparing 3D assets for simulation. A new blueprint for integrating Omniverse libraries in Blender is also now available on GitHub.

The libraries’ key capabilities include:

• NVIDIA RTX sensor simulation: ovrtx helps applications generate camera, lidar, radar and other sensor outputs from 3D scenes, so developers and AI agents can test how physical AI systems may perceive virtual environments.
• Physical behavior: ovphysx uses GPU-accelerated physics to bring realistic behavior to 3D scenes using properties such as collisions, mass, friction and motion, so teams can first test how objects and systems interact in simulation.
• Simulation-ready 3D objects: CAD-to-SimReady skills help convert computer-aided design (CAD) data to SimReady assets built on OpenUSD, giving 3D content the properties needed for physical AI simulation and virtual testing.

Software Makers Build With Omniverse Libraries
Software makers including SideFX and PTC, as well as startups ForgeCAD, Lightwheel, Moonlake AI and Palatial, are among the first to adopt and build with Omniverse libraries, now part of NVIDIA Agent Toolkit.

SideFX is using OpenUSD workflows, as well as ovrtx and ovphysx libraries, to explore how agents can help integrate Omniverse libraries into its Houdini procedural 3D content creation workflows, giving technical artists a path to generate, test physics and prepare content for simulation.

“Procedural 3D creation is essential to building the complex, controllable worlds needed for simulation, robotics and industrial AI,” said Kim Davidson, president and CEO of SideFX. “With NVIDIA Omniverse libraries and OpenUSD, SideFX is exploring how agent-ready tools can support Houdini workflows, helping technical artists review, test and prepare procedural content for simulation while staying in control of the creative process.”

The PTC Onshape CAD and product data management (PDM) platform is using OpenUSD and ovrtx to connect cloud-native design workflows with physical simulation, helping product design content stay connected with CAD, PDM, collaboration and simulation workflows.

“Engineering teams are seeking more connected ways to design, collaborate and simulate throughout the development process,” said Neil Barua, president and CEO of PTC. “PTC’s work with NVIDIA supports that broader vision, while NVIDIA Omniverse libraries help enable simulation-ready workflows that bring validation and testing closer to where products are designed.”

On display at SIGGRAPH, “SimReady” Blender is a sample workflow built in Blender with NVIDIA Omniverse libraries and NVIDIA NemoClaw™, showing how software makers can add agent-ready simulation capabilities — including NVIDIA RTX sensor simulation, physics and validation — into existing 3D applications while keeping creators in control. This is now openly available as a blueprint for integrating Omniverse libraries in Blender.

The demo also previews how these workflows, built with Omniverse libraries as part of NVIDIA Agent Toolkit, can run locally, from compact RTX-powered systems with NVIDIA RTX Spark™ to NVIDIA GB300-powered systems with NVIDIA DGX Station™. RTX Spark systems will be available this fall from ASUS, Dell Technologies, HP, Lenovo, Microsoft Surface and MSI, with models from Acer and GIGABYTE to follow. DGX Station systems are available to order from ASUS, Dell, GIGABYTE, HP, MSI, Supermicro and Exxact.

Startups, including those part of the NVIDIA Inception program, are also using Omniverse libraries and skills to add agent-assisted asset and scene preparation workflows. Palatial is using Omniverse CAD-to-SimReady skills to automate the creation and validation of SimReady assets at scale from CAD inputs. Lightwheel is using Omniverse Content Agents powered by OpenUSD in its SimReadyGen technology to generate physically accurate SimReady assets from text prompts.

ForgeCAD and Moonlake AI are exploring agent-driven 3D content workflows that use Omniverse capabilities to help generate, augment and prepare assets for physical AI simulation.

Watch the NVIDIA keynote at SIGGRAPH. Learn more about NVIDIA Omniverse libraries and explore available samples and documentation.

The post NVIDIA Agent Toolkit Expands With New Omniverse Libraries, Putting AI Agents to Work Building Simulation-Ready Worlds appeared first on Tech Scene ZA.

News Summary:

• Industry and research leaders including Dassault Systèmes, Databricks, Lilly, Schrödinger, Snowflake, the UW Medicine Institute for Protein Design and dozens others are adopting, and Anthropic and OpenAI are integrating, NVIDIA BioNeMo Agent Toolkit to bring agentic life sciences workflows to researchers and scientists.
• NVIDIA BioNeMo Agent Toolkit — including NVIDIA Nemotron, NemoClaw, OpenShell and BioNeMo — gives agents accelerated life sciences tools spanning biology, chemistry, genomics and drug discovery.
• New NVIDIA BioNeMo tools give agents the context and know-how to execute scientific computing — improving accuracy, task completion and token efficiency.

BIO—NVIDIA today announced NVIDIA BioNeMo Agent Toolkit, which provides domain-specific tools and skills for the agentic life sciences era.

Including more than a decade’s worth of NVIDIA life sciences libraries, tools and open models, the toolkit enables AI agents, scientists and labs to work together by gathering evidence, reasoning across findings, running computational experiments and recommending the next best steps to accelerate discovery.

It gives any agent or AI platform — from general-purpose assistants to specialized scientific agents, software platforms and in-house biopharma systems — the tools needed to synthesize and summarize scientific knowledge, call models, evaluate results, reason and execute next actions.

The toolkit includes NVIDIA BioNeMo™ and is powered by NVIDIA NIM™ microservices, NVIDIA Parabricks^®, NVIDIA NeMo™ and NVIDIA Nemotron™ technologies, along with accelerated computing and skills — providing an open and trusted foundation for agentic life sciences.

More than 50 leading companies are already using it to advance scientific discovery, tapping into agent-callable skills for tasks including protein structure prediction, molecular docking, generative chemistry, genomic analysis, protein design and biomarker discovery.

“Frontier models are the brains. BioNeMo is the scientific toolbox. Together, they give AI agents the skills of a PhD research assistant and the speed of a supercomputer,” said Jensen Huang, founder and CEO of NVIDIA. “For the first time, researchers can build AI agents that understand scientific knowledge, use scientific tools and execute scientific workflows. This is a new way to do science — one that can dramatically accelerate discovery across biology, chemistry, genomics and medicine.”

Open model and research organizations — including the Arc Institute, Open Molecular Software Foundation and the University of Washington’s Institute for Protein Design (IPD) — are working with NVIDIA to use BioNeMo to advance frontier models and make them more accessible through agent-ready workflows. The IPD collaboration has accelerated runtimes for state-of-the-art biodesign models like RosettaFold3, resulting in 2x faster performance than the prior-generation model, and many additional applications to accelerate protein design efforts are ongoing, giving researchers tools at a scale and cost not before possible.

“Every tool we’ve built for protein design is only as powerful as the scientists who can efficiently access it,” said David Baker, professor of biochemistry at the University of Washington School of Medicine and director of the Institute for Protein Design. “The next leap in science won’t come from a single discovery; it will come from the speed of iterative designs and agents that can repeatedly reason through the complexity of biology at a speed humans never could.”

Agent-Ready Tools and Skills for Life Sciences
Life sciences is one of the world’s most important scientific frontiers, with global scientific R&D reaching $3.8 trillion and annual pharmaceutical budgets approaching $300 billion.

Agentic workflows can help the industry iterate faster while reducing costs and maximizing the probability of success. With the toolkit allowing developers to transform general-purpose agents into life sciences agents in minutes, researchers can run experiments faster, continuously learn from results and close the loop between hypothesis and discovery, with some companies extending this iteration into physical labs.

A general-purpose agent can struggle to navigate scientific workflows efficiently, needing to infer the correct tools, inputs, outputs and biological meaning along the way. With BioNeMo Agent Toolkit, agents can call the right tools, interpret results more accurately and gain scientific insights faster and more reliably.

NVIDIA is optimizing the entire BioNeMo platform by turning libraries, models and frameworks into agent-callable tools.

This includes harnessing NVIDIA Agent Toolkit technologies such as NVIDIA Nemotron open models for the reasoning foundation, the NVIDIA NeMo RL library for reinforcement learning and NVIDIA NemoClaw™ blueprints for secure, private agents that can reason across tasks, call tools and interact with data continuously.

NVIDIA NIM microservices help agents call models and perform tasks. The NVIDIA OpenShell™ runtime provides a controlled executable environment.

The toolkit’s components enable agents to complete workflows such as:

• Virtual Screening: Agents can help researchers identify small-molecule drug candidates by generating and screening compounds, docking them to a target, predicting binding strength and filtering for drug-like properties. Then, the agent can output which candidates should be prioritized, compressing screening timelines from days to minutes.
• Genomic Analysis and Target Discovery: Agents can help researchers transform raw sequencing data into prioritized genetic insights and biological targets. NVIDIA Parabricks accelerates alignment and variant calling, while genomic foundation models score variant effects and the agent ranks the most disease-relevant candidates for further study.
• Protein Binder Design: Agents can help researchers design and validate candidates computationally before work begins, compressing traditionally labor-intensive design work.
• Deep Biomedical Research: Agents connect real-world data to reasoning models to improve the efficiency and accuracy of various scientific and clinical development processes, including literature review, protocol generation, clinical trial screening and pharmacovigilance with the NVIDIA Biomedical AI-Q Research Agent.
• Medical Imaging Analysis: Agents can help researchers process, segment, synthesize and reason over medical imaging data to support biomarker discovery, accelerating evidence generation across research workflows.

Life Sciences Ecosystem Builds With NVIDIA BioNeMo
Companies across the technology and life sciences ecosystem are using the toolkit to advance agentic workflows.

Frontier labs and scientific agent builders including Anthropic, Edison Scientific, Lila Sciences, OpenAI and Owkin are integrating with BioNeMo to help agents move from answering questions to completing scientific work. NVIDIA accelerated models and analysis libraries help shorten the time from hypothesis to insight.

Scientific data and workflow platforms from Benchling, Certara, Databricks, Snowflake and Seqera are using BioNeMo Agent Toolkit to connect data systems with AI-powered science. BioNeMo skills can help agents query biological and chemical datasets, prepare model-ready inputs, launch reproducible workflows, analyze outputs and return insights directly within the platforms scientists and data teams already use daily.

Diagnostics and pharmaceutical companies including Lilly and Natera are using BioNeMo Agent Toolkit to scale repeatable agentic workflows across discovery, translational research and clinical insight.

AI-native biology companies including Boltz, Basecamp Research, Chai Discovery, Dyno, PerturbAI and Proxima have collaborated with NVIDIA to develop tools to accelerate model-powered therapeutic design workflows.

Computer-aided drug discovery software providers including Dassault Systèmes, Cadence (OpenEye) and Schrödinger are integrating the toolkit’s capabilities into scientific applications used across discovery teams. Then, agents can help orchestrate molecular generation, docking and prediction, turning computer-aided design platforms into systems where researchers can ask questions, launch analyses and identify next-best actions faster.

Lab instruments and automation companies including Automata, HighRes, Tecan, Thermo Fisher and autonomous data generation platform Medra are connecting systems with computational discovery powered by BioNeMo skills.

AI clouds and AI infrastructure companies including Baseten, Modal and Nebius are using the toolkit to help developers build life sciences workflows as reliable hosted services. By supporting BioNeMo skills and tools through scalable application programming interfaces, managed compute and production inference environments, these companies can help move agentic biology workflows from prototypes into accessible services for researchers and enterprises.

Availability
BioNeMo Agent Toolkit and skills are available now through the NVIDIA developer resources page and GitHub.

The post NVIDIA Announces BioNeMo Agent Toolkit — Tools for Agents to Accelerate Scientific Discovery appeared first on Tech Scene ZA.