TÜV SÜD, Accenture and NVIDIA Unite to Fast‑Track AI Robot Safety Certification in Factories and Warehouses

TÜV SÜD and Accenture have unveiled their new “Physical AI Safety Suite” at NVIDIA GTC Berlin 2026, a framework designed to accelerate certification of autonomous robots and AI-driven systems in factories and warehouses. The suite uses digital twins, AI agents, and simulations to reduce certification timelines by up to 30%, helping manufacturers deploy automation faster and more safely.

Established industry standards form the foundation for ensuring that automated facilities are both safely designed and reliably operated. A key example is ISO 3691‑4, which outlines safety requirements for driverless industrial trucks and systems, including Autonomous Mobile Robots (AMRs). In many industrialized nations, certification against such standards is mandatory before companies can commission automated facilities or deploy new physical AI systems. Even in regions where certification is voluntary, adherence to recognized industry standards remains best practice.

However, navigating this complex landscape of overlapping standards often slows the adoption of physical AI‑enabled automation. At a mid‑sized production plant, the iterative process of coordinating, validating, and gaining approval for safety concepts can extend over several weeks, delaying deployment timelines.

At NVIDIA GTC Berlin, TÜV SÜD introduced the Physical AI Safety Suite, a system that uses digital twins and AI simulations to replace parts of the manual safety checks for factories and equipment. This digital approach can bring production online up to 30% faster. By reducing the need for physical site visits and installed equipment, companies can spot safety and certification issues earlier, avoiding expensive fixes later in operation.

Key Highlights

• Launch Event: Presented at NVIDIA GTC Berlin 2026
• Collaborators: TÜV SÜD and Accenture
• Core Technology: Digital twins, AI agents, and simulation testing
• Target Use Cases: Autonomous Mobile Robots (AMRs), driverless industrial trucks, warehouse automation
• Productivity Impact: 30% faster commissioning of automated facilities
• Standards Basis: Built on ISO 3691-4 safety standards

How the “Physical AI Safety Suite” Works

Using digital twins, AI agents and simulations, TÜV SÜD and Accenture’s Physical AI Safety Suite is designed to speed up the automation of factories and warehouses. (Image - Accenture.com)

• Digital Certification: Automates manual inspection and certification package creation
• Safety Scenarios: Simulates risk scenarios beyond physical testing
• Early Compliance: Validates safety concepts earlier in design cycle
• Continuous Monitoring: Supports ongoing compliance during operations

Benefits for Manufacturers

Benefit	Details
Faster Certification	Cuts commissioning timelines by ~30%
Improved Safety	Simulates scenarios beyond real-world testing limits
Cost Efficiency	Reduces rework and delays in automation rollout
Scalability	Supports deployment across diverse environments

Risks & Challenges

• Complex Standards Navigation: Certification requires compliance with multiple overlapping standards
• Integration Costs: Simulation infrastructure may be expensive for smaller manufacturers
• Human-Robot Interaction Risks: Real-world unpredictability remains a challenge
• Regulatory Variability: Certification requirements differ across countries

Context for India & Global Manufacturing

For manufacturers in India and Asia: TÜV SÜD and Accenture’s framework could help reduce bottlenecks in certifying AMRs and AI-driven logistics systems. This is particularly relevant for semiconductor fabs, automotive plants, and large-scale warehouses in Gurugram and Bengaluru, where AI-enabled automation is expanding rapidly.

AM Intelligence Expands NVIDIA Vera Rubin AI Factory Buildout with 29,000 GPUs and 100 MW Capacity Across India and Malaysia

Two further binding orders of ~20,000 NVIDIA GPUs take AMI's committed NVIDIA Vera Rubin capacity to approximately ~29,000 GPUs and close to 100 MW, part of its global development pipeline of ~400 MW

AM Intelligence (“AMI”), the AI infrastructure platform set up by Promoters of Greenko, today announced two further firm and binding orders for 20,000 NVIDIA Rubin GPUs, to be deployed as NVIDIA Vera Rubin NVL72 rack-scale systems. Building on its first AI factory in Hyderabad, the expansion is designed to give cloud providers, enterprises and AI developers access to large-scale computing capacity closer to where they build and deploy AI.

These orders are in addition to the AMI's inaugural commitment announced on August 25, 2026, for 9,000 NVIDIA Rubin GPUs at its first AI Factory in Hyderabad.

This follow-on capacity totalling ~70 megawatts (MW) will be deployed at Malaysia and India with delivery scheduled for Q2 2027.

Taken together, the three orders bring AMI's committed frontier compute to approximately 29,000 Rubin GPUs and close to 100 MW of capacity, establishing AMI among the earliest and largest committed buyers of Rubin GPUs outside of North America.

AMI plans to bring additional 300 MWs of capacity to market in the next 15 months, involving capital expenditure of more than $20+ billion in addition to the $6 billion already committed for the initial $100 MW.

“Both facilities will be built to the same architecture standard as the Hyderabad AI Factory, integrating the NVIDIA Vera Rubin platform with high-throughput RDMA over Converged Ethernet (RoCE) networking for fast, efficient data movement, alongside advanced storage systems,” said Anil Chalamalasetty, Chairman, AM Intelligence. “Each site is engineered for high rack power density and liquid cooled throughout, allowing the infrastructure to accommodate successive generations of AI silicon within the same physical footprint.”

“AM Intelligence is bringing the next generation of AI factories to India and Malaysia with NVIDIA Vera Rubin,” said Vishal Dhupar, managing director, Asia South, NVIDIA."By combining accelerated computing with high-performance networking and liquid-cooled infrastructure, AMI is creating the foundation for developers and enterprises to build more ambitious AI.”

AMI’s ~400 MW Global Pipeline

AMI has developed a pipeline of ~400 MW of Compute-as-a-Service capacity across global locations, including India, the United States, Europe and Malaysia. The three orders placed to date represent the first committed tranche within that pipeline, and the balance is under development for delivery in subsequent phases in 2027 and early 2028.

The pipeline advances AMI's stated intention to bring 1 GW of Compute-as-a-Service capacity to global AI workloads, within a broader programme of 5 GW of powered AI data centres across India, the United States and Europe.

AMI, as a strategic builder of long-term assets, will leverage its unique power solutions, capex advantage and access to next-generation AI infrastructure to deliver best-in-class electron-to-token economics by integrating energy infrastructure with the computing capacity required to train and run AI models. This will enable AI training and inference at scale with a collaborative service offering spanning hyperscalers, neo cloud providers, sovereign AI initiatives, frontier labs, AI natives and local developer communities.

About AM Intelligence:

AM Intelligence (AMI) is building a full-stack AI infrastructure ecosystem spanning energy, data-center infrastructure, hardware, and customized AI models. Backed by Greenko's owned renewable generation and storage, AMI is developing gigawatt-scale, liquid-cooled compute capacity designed to serve global hyperscalers, frontier AI labs, enterprises, and India's sovereign AI initiatives — while widening chipset access for Indian developers building domestic and global AI solutions. AM Intelligence is developing 5 GW of powered AI data centres and 1 GW of Compute-as-a-Service across India, United States and Europe, with 400 MW of Compute-as-a-Service capacity coming to market in the near term.

AMI, promoted by the founders of Greenko, is building globally differentiated platforms across energy, molecules and AI with a focus on enabling next generation of intelligent infrastructure. On electrons, we are building a 50 GW energy infrastructure platform across 20+ states, spanning solar, wind, hydro and energy storage. We are also developing the world’s first interconnected energy storage platform, targeting 200 GWh of storage capacity across India.

On molecules, we are developing low-carbon molecule platform, including green ammonia, with a target of 5 MTPA of green ammonia capacity across multiple locations in India. Our 1 MTPA green ammonia project currently under construction is expected to be among the world’s largest RFNBO-compliant green ammonia facilities, supporting energy diversification for global markets for multitude of use cases across power generation, mobility amongst others. For more information on AM Intelligence and its AI infrastructure platform, visit: https://amintelligencegroup.com/

AM Intelligence Orders 9,000 NVIDIA Rubin GPUs to Launch Asia’s First Frontier AI Cluster in Hyderabad

AMI has placed binding order for ~9,000 NVIDIA Rubin GPUs deployed as Vera Rubin NVL72 rack-scale systems in its first AI Factory in Hyderabad, part of its 1 GW compute capacity globally

AM Intelligence ("AMI"), the AI infrastructure platform set up by Promoters of Greenko, is striving to become the World’s leading, intelligence infrastructure company, powering the AI economy through vertically integrated energy, compute and innovation.

AMI today announced first step towards this vision by placing a firm and binding order for Vera Rubin delivery in Q1 2027 at its first AI factory in Hyderabad. The order covers 9,000 NVIDIA Rubin GPUs deployed as Vera Rubin NVL72 rack-scale systems for the 30 MW of capacity and will make AMI’s Hyderabad AI Factory as one of the first frontier AI compute clusters in Asia.

The Hyderabad facility will be the first tranche of AMI's planned 1 GW of Compute-as-a-Service capacity across India, the United States, Finland and Malaysia. AMI plans to bring an initial 200 megawatts (MW) of capacity to market in the near term, involving capital expenditure of more than USD 8 billion.

Anil Chalamalasetty, Chairman, AM Intelligence, said: “For over two decades, we have focused on transforming electrons into value — addressing critical and emerging societal needs. Today, the electron-to-token opportunity allows us to take this capability further, converting power infrastructure into frontier AI compute at scale. Bringing latest generation NVIDIA Vera Rubin to India marks the next step in this journey and establishes AMI at the forefront of the emerging AI electron-to-token economy.”

By integrating NVIDIA Vera Rubin architecture with high-throughput RDMA over Converged Ethernet (RoCE) networking for fast, efficient data movement, and advanced storage systems, the Hyderabad AI Factory is being designed to run some of the world’s largest and most sophisticated AI models, including trillion-parameter models and next-generation agentic AI applications.

NVIDIA Vera Rubin NVL72 introduces NVFP4, a new low-precision computing format designed to run AI workloads more efficiently, alongside next-generation High Bandwidth Memory (HBM4). The architecture is designed to reduce AI inference token-serving costs by up to 10 times compared with the preceding NVIDIA Grace Blackwell generation. AMI’s Hyderabad AI Factory is being engineered to deliver approximately 450 exaFLOPS of NVFP4 inference compute, providing massive computing capacity for running advanced AI models at scale.

AMI: Building Toward 5 Gigawatts

Over the last two decades, our focus has been on electron valorisation, creating progressively greater value from energy across energy management, molecules and metals, and now AI tokens, the units of information processed and generated by AI models. This end-to-end approach has been central to our philosophy of addressing critical and emerging societal needs.

Today, intelligence has reached the status of permeating humankind’s lives on a daily basis. AMI, as a strategic builder of long-term heavy assets, will leverage its unique power solutions, capex advantage and access to next-generation NVIDIA AI infrastructure to deliver highly cost-competitive electron-to-token economics by integrating energy infrastructure with the computing capacity required to train and run AI models. This will enable wider deployment of AI with a collaborative service offering spanning across hyperscalers, neo cloud providers, sovereign AI initiatives, frontier labs, AI natives and local developer communities.

AMI is developing 5 GW of powered AI data centres across India, the United States and Europe. These facilities are being designed for high-density AI computing, using liquid cooling to manage the significant power and heat requirements of advanced AI chips, while allowing the infrastructure to accommodate successive generations of AI silicon. AMI also intends to bring 1 GW of Compute-as-a-Service capacity to global AI workloads with initial capacity of 200 MW coming online in short-term.

About AM Intelligence:

AM Intelligence (AMI) is building a full-stack AI infrastructure ecosystem spanning energy, data-center infrastructure, hardware, and customized AI models. Backed by Greenko's owned renewable generation and storage, AMI is developing gigawatt-scale, liquid-cooled compute capacity designed to serve global hyperscalers, frontier AI labs, enterprises, and India's sovereign AI initiatives — while widening chipset access for Indian developers building domestic and global AI solutions. AM Intelligence is developing 5 GW of powered AI data centres and 1 GW of Compute-as-a-Service across India, United States and Europe, with 200 MW of Compute-as-a-Service capacity coming to market in the near term.

AMI, promoted by the founders of Greenko, is building globally differentiated platforms across energy, molecules and AI with a focus on enabling next generation of intelligent infrastructure. On electrons, we are building a 50 GW energy infrastructure platform across 20+ states, spanning solar, wind, hydro and energy storage. We are also developing the world’s first interconnected energy storage platform, targeting 200 GWh of storage capacity across India.

On molecules, we are developing low-carbon molecule platform, including green ammonia, with a target of 5 Mtpa of green ammonia capacity across multiple locations in India. Our 1 Mtpa green ammonia project currently under construction is expected to be among the world’s largest RFNBO-compliant green ammonia facilities, supporting energy diversification for Global markets for multitude of use cases across power generation, mobility amongst others.

L&T's Vyoma.AI Secures India’s Largest NVIDIA B300 AI Factory to Power Global AI Cloud Innovation

Representative Image

• Marking its foray into AI Factory business, L&T will deploy NVIDIA B300 infrastructure at its Chennai data centre campus to power Together AI’s AI-native cloud platform for large-scale training and inference.

Vyoma.AI, an L&T company, through its AI infrastructure subsidiary LTN Compute, has secured India's largest single-cluster AI infrastructure - an NVIDIA B300 AI Factory - for a US-based AI cloud innovator.

The AI Factory will power AI Native Cloud platform for large-scale inference, fine-tuning and training workloads, strengthening India's AI infrastructure ecosystem while supporting global innovation.

Vyoma's Chennai data centre campus, a gigawatt-scale AI infrastructure site, with Phase 1 designed for 250 MW and power infrastructure readiness of 150 MVA, provides a scalable foundation for future AI Factory expansion.

The integrated AI Factory, with capacity of 10,000 B300 NVIDIA GPUs, will be hosted at Vyoma’s Chennai data centre. The platform combines hyperscale data centre infrastructure, accelerated computing, high-performance networking, ultra-low-latency interconnects, high-throughput parallel storage and AI infrastructure operations, enabling customers to seamlessly deploy and scale AI workloads through a unified, end-to-end AI infrastructure stack.

"Artificial Intelligence is becoming foundational to every industry and AI Factories will power this transformation. Our deployment of an NVIDIA B300 AI Factory for Together AI marks a significant milestone in L&T's Gigawatt AI Infrastructure Mission and reinforces our commitment to making India a global hub for next-generation AI infrastructure", said Mr S N Subrahmanyan, Chairman & Managing Director, Larsen & Toubro.

"Making AI globally accessible is going to be the biggest infrastructure build-out in human history, and L&T understands that”, said Mr Vipul Ved Prakash, Co-founder & CEO, Together AI. “That's exactly why we partnered with them - to bring the scale, resilience and engineering excellence this moment demands to India.”

LTN Compute, a subsidiary of Vyoma.AI, is building AI-ready digital infrastructure across India through hyperscale AI datacentres, sovereign cloud platforms, AI Factory services, GPU-as-a-Service (GPUaaS) and managed AI platforms to support governments, enterprises, cloud providers and AI innovators.

IBM, Together AI Launch $240M NVIDIA-Powered Inference Cluster on IBM Cloud

NVIDIA HGX B300

• First large-scale inference cluster with Together AI on IBM Cloud using NVIDIA HGX B300 systems to help enterprises run AI workloads, designed for fast and efficient production

IBM (NYSE: IBM) announced a collaboration with Together AI to deliver IBM and NVIDIA AI infrastructure. Under a multi-year $240M agreement between IBM and Together AI, IBM is positioned to deploy a large cluster of NVIDIA HGX B300 systems on IBM Cloud with expected availability in Q1 2027. Together AI will use this cluster to provide open-source model inference. This deployment is the first dedicated, large-scale cluster built for inference on IBM Cloud using HGX B300 systems and NVIDIA Spectrum-XTM Ethernet networking. According to NVIDIA, it is built to deliver 30x more AI factory output compared to prior generations.

This collaboration aims to enable Together AI to deliver better performance and token economics to enterprises as they look to efficiently scale their AI deployments. Together AI is built on the principle that open-source models are essential for the future of AI and developers should be able to build using open, modular stacks. The company recently raised an $800M Series C financing round at an $8.3B valuation to expand its AI Native Cloud and its platform spans capabilities across inference, training, fine tuning, and agentic workflows. The company reports that it has seen significant momentum for its inference product, now serving 400 trillion tokens monthly.

Together AI selected IBM with NVIDIA because of their innovative product roadmaps and their ability to deliver GPU capacity at the pace required for rapid AI scaling and lowest token cost. Building on IBM's expertise in delivering enterprise-grade cloud capabilities, this collaboration aims to help Together AI to continue its expansion into the enterprise space while making open-source AI more accessible to developers and enterprises around the world.

"Enterprises want the performance of the best frontier models without the closed-model price tag, and that only works if the infrastructure underneath is fast and reliable at scale," said Vipul Ved Prakash, CEO at Together AI. "Working alongside IBM with NVIDIA gives us that foundation. This cluster lets us bring production-grade inference to more companies, faster, and it's a big step in our push to make open-source AI the obvious choice for enterprises."

IBM and NVIDIA Expand Collaboration to Power the Next Wave of AI Innovation

"Enterprises are in a race to adopt agentic AI at scale to drive real business outcomes," said Alan Peacock, General Manager of IBM Cloud. "IBM and NVIDIA are delivering scalable, economical, enterprise-grade AI infrastructure that can help Together AI accelerate innovation for the next generation of AI infrastructure." 

"AI factories are becoming essential enterprise infrastructure—like electricity and telecommunications—turning compute and data into intelligence," said Dion Harris, Senior Director, HPC and AI Infrastructure Solutions, NVIDIA. "With NVIDIA HGX B300 systems and NVIDIA Spectrum-X Ethernet networking on IBM Cloud, IBM and Together AI will deliver an accelerated computing platform to help enterprises deploy open-source AI with the performance, efficiency and scale required for real-time AI services."

This work is the latest example of a larger collaboration between IBM and NVIDIA, who continue to work together to advance AI across infrastructure and software, to deliver performance and efficiency for enterprise and startup clients. A hybrid environment on IBM Cloud powered by NVIDIA GPUs connected with NVIDIA Spectrum-X Ethernet networking and Together AI's inference platform gives organizations a reliable foundation to build, deploy and scale AI systems. Additionally, IBM and NVIDIA recently announced progress across GPU-native data analytics, unstructured data extraction, on-premises and cloud infrastructure, and consulting services. These advancements are designed to help organizations operationalize AI at scale. For more information about the IBM and NVIDIA collaboration, visit www.ibm.com/products/gpu-ai-accelerator/nvidia.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

About Together AI

Together AI is the AI Native Cloud, combining state-of-the-art open-source models, high-performance infrastructure, and frontier research in AI efficiency and scalability. Founded in 2022, Together AI powers over a million of developers and some of the world's most demanding AI workloads, delivering production-scale inference, training, and reinforcement learning for the next generation of AI-native companies.

NVIDIA Unveils Nemotron 3.5 Lightning: 30 Billion Open‑Source AI Model for Faster Agentic Workflows

NVIDIA has just launched Nemotron 3.5 Lightning, a 30-billion-parameter open-source AI model optimized for agentic workflows. It delivers up to 4× faster output and 30% quicker task completion compared to peers, while being lightweight enough to run locally on a single GPU — even on laptops and desktops.

Key Highlights

• Architecture: Mixture-of-Experts (MoE) hybrid with Mamba-2 + Attention layers.
• Parameters: 30B total, 3B active per token (efficient activation).
• Performance: Up to 4× faster output, 30% faster agentic task completion, PinchBench accuracy: 86%.
• Deployment: Runs on RTX PCs, Jetson devices, DGX Spark, workstations, data centers, and cloud.
• Open-source availability: Free to download via HuggingFace, NVIDIA NGC, and build.nvidia.com.
• Use cases: Code review, cybersecurity monitoring, billing queries, tool calls, and multi-agent workflows.

Comparison with Other Models

Model	Parameters	Specialization	Performance	Deployment
Nemotron 3.5 Lightning	30B (3B active)	High-volume agent execution	4× faster, 30% quicker tasks	Single GPU, edge, cloud
Nemotron 3 Ultra	Larger frontier model	Planning & orchestration	Advanced reasoning	Data center/cloud
Qwen3.6 35B	35B	General-purpose	Slower task completion	Cloud-heavy

Technical Features

• Multi-token prediction: MTP + speculative decoding for concurrency.
• Draft models: DSpark (DGX Spark inference), DFlash (workload evaluation).
• Formats: BF16 (full precision) and NVFP4 (optimized inference).
• Context length: Up to 1M tokens, enabling long workflows.
• Supported languages: English, Spanish, French, German, Italian, Japanese, plus coding languages.

Strategic Context

• Open-weight AI: Jensen Huang emphasized open-weight AI as critical for innovation, lowering entry barriers for enterprises while driving GPU demand.
• Commercial use: Free to use commercially, unlike proprietary subscription-based AI services.
• Concerns: National security risks and IP leakage remain, but NVIDIA positions Nemotron as a cornerstone for agentic AI ecosystems.

TCS Launches Autonomous Engineering Lab with NVIDIA to Accelerate Industrial AI Deployment

Tata Consultancy Services (TCS), a global leader in IT services and consulting, has announced the launch of the TCS Autonomous Engineering Lab Powered by NVIDIA at its Global Axis campus in Bengaluru. This new facility is positioned as India’s first physical AI hub dedicated to accelerating industrial AI solutions, with a focus on mobility and manufacturing.

The lab represents a strategic expansion of TCS’ collaboration with NVIDIA, moving beyond technology integration into deeper capability building and enterprise-wide transformation. By combining TCS’ Industrial Autonomy & Engineering (IA&E) expertise with NVIDIA’s full-stack AI infrastructure, the lab provides enterprises with a scalable pathway to move from pilot projects into production-scale deployment.

Sreenivasa Chakravarti, Global Head & Vice President of Industrial Autonomy and Engineering at TCS, described Bengaluru as the “engine of India’s economy,” noting that the lab harnesses this energy to reimagine what’s possible with AI. He emphasized that the facility will enable rapid movement from concept to real-world impact, shaping the future of mobility and industrial systems. Alvin DaCosta, Vice President at NVIDIA, highlighted the importance of specialized infrastructure to bridge the gap between simulation and deployment, stressing that the lab offers enterprises a robust environment to validate and implement next-generation solutions.

The lab’s offerings span a wide spectrum of industrial AI applications. TCS DriveSphere™, an AI-led mobility platform, enables software-defined vehicles through digital twins, predictive analytics, and lifecycle management. Advanced driver assistance systems (ADAS), autonomous driving capabilities, and intelligent perception systems are supported by NVIDIA’s AI stack. In manufacturing, AI is embedded into machines and plants to enable predictive maintenance, automated quality inspection, and real-time process optimization. High-fidelity digital twin simulations further accelerate design validation and scenario testing, reducing risks and improving time-to-market.

Beyond technology, the lab is designed to help enterprises reimagine products, operations, and business models. By embedding intelligence across the engineering and manufacturing lifecycle, businesses can transition toward intelligent, software-defined, and autonomous enterprises. The controlled environment allows for rapid prototyping and validation, ensuring that solutions are tested thoroughly before rollout.

This initiative builds on the longstanding partnership between TCS and NVIDIA in AI and accelerated computing. With the Bengaluru lab, the collaboration enters a new phase—one that emphasizes scaled deployment, client engagement, and transformative impact across industries worldwide.

For a global audience, the launch underscores India’s growing role as a hub for industrial AI innovation, while also highlighting how enterprises worldwide can leverage this facility to accelerate their journey toward autonomy and intelligence.

Nvidia Teaches Self-Driving Cars to Think, Not Just React

Nvidia’s new self-driving car model, Alpamayo, takes a clever leap by focusing on reasoning rather than just reaction—making cars think more like humans and handle unpredictable road scenarios safely.

Nvidia’s Alpamayo: The “ChatGPT of Self-Driving

- Reasoning over reaction: Unlike traditional AV systems that rely on pattern recognition, Alpamayo introduces a vision-language-action (VLA) model that can interpret, reason step-by-step, and decide the safest driving action.

- Human-like judgment: It’s designed to handle the “long tail” of rare, risky scenarios—like sudden roadworks or erratic driver behavior—that older models struggle with.

- Transparency & trust: Alpamayo explains its decisions, making it easier for regulators and passengers to understand why a car acted a certain way.

- Open ecosystem: Nvidia released Alpamayo as an open-source portfolio of models, simulation frameworks, and datasets, allowing developers to build on it without starting from scratch.

Key Features of Alpamayo

Feature	Why It’s Clever	Impact
Vision-Language-Action (VLA) Model	Breaks down problems into reasoning steps	Safer navigation in complex traffic
Chain-of-Thought AI	Mimics human decision-making	Handles unpredictable “edge cases”
Open-Source Tools	Available via Hugging Face	Accelerates industry adoption
Simulation Frameworks	Test rare scenarios virtually	Faster validation & training
Explainability	Cars can justify their actions	Builds trust with regulators & riders

Challenges & Trade-Offs

Computational demand: A 10-billion parameter model requires immense processing power, which could raise costs.

Regulatory hurdles: While explainability helps, regulators may still be cautious about approving reasoning-based autonomy.

Edge-case reliance: Success depends on how well Alpamayo generalizes across diverse geographies and driving cultures.

Why This Matters

Nvidia’s approach is clever because it shifts the narrative from “self-driving cars that react” to “self-driving cars that think.” By making reasoning explicit, Alpamayo could become the backbone of Level 4 autonomy, where cars drive themselves in most conditions without human intervention.
This is being hailed as the “ChatGPT moment for physical AI”—bringing conversational reasoning into the world of machines that move. If successful, Alpamayo could redefine trust in autonomous vehicles, making them safer, more transparent, and more adaptable to real-world chaos.

Cisco and NVIDIA Introduce AI‑Native 6G Wireless Stack, Redefining Cloud and Enterprise Infrastructure

Cisco and NVIDIA have announced a broad set of AI infrastructure innovations designed to accelerate adoption of artificial intelligence across cloud, enterprise, and telecom sectors. The collaboration brings together Cisco’s networking and security expertise with NVIDIA’s AI computing leadership, marking what executives described as the beginning of the “largest data center build‑out in history.”

Spectrum‑X Powered Switches

At the center of the announcement is the Cisco N9100 Series data center switch, the first NVIDIA partner‑developed switch built on NVIDIA Spectrum‑X Ethernet technology. The switch is designed to deliver high‑performance, low‑latency networking for AI workloads and will be available with both NX‑OS and SONiC operating models. Cisco said the platform will serve as a Cloud Partner‑compliant reference architecture, enabling neocloud and sovereign cloud providers to deploy AI infrastructure at scale.

Enterprise AI Security and Observability

Cisco also expanded its Secure AI Factory with NVIDIA, a framework that integrates compute, networking, security, and observability into enterprise AI deployments. The initiative aims to give organizations end‑to‑end visibility and protection as they scale AI workloads, particularly in regulated industries. New ecosystem partnerships were announced to strengthen monitoring and compliance capabilities.

Telecom and 6G Readiness

In a move aimed at telecom operators, Cisco and NVIDIA unveiled the industry’s first AI‑native wireless stack for 6G networks. The stack is designed to handle ultra‑low latency and massive device connectivity, preparing carriers for the surge in AI‑driven traffic expected over the next decade. Analysts said the development could redefine mobile networks by enabling real‑time AI services at the edge.

Strategic Context

Executives from both companies emphasized that the innovations are not standalone products but part of a joint reference architecture for next‑generation AI deployments. “We are entering a new era where AI workloads will reshape every industry,” said a Cisco spokesperson. “Our partnership with NVIDIA ensures customers have the flexibility, interoperability, and scalability to build AI infrastructure securely and globally.”

Why It Matters

• For Cloud Providers: A unified, NVIDIA‑compliant architecture accelerates AI adoption in sovereign and neocloud environments.
• For Enterprises: Enhanced security and observability ensure safer AI deployments.
• For Telecoms: The AI‑native 6G stack positions operators to deliver next‑generation services.

With these announcements, Cisco and NVIDIA are positioning themselves at the heart of the global AI infrastructure race, targeting the needs of hyperscalers, enterprises, and telecom operators alike.

Nvidia Commits $100 Billion to OpenAI in Historic AI Infrastructure Partnership

In a move set to redefine the global artificial intelligence landscape, Nvidia has announced a strategic partnership with OpenAI that includes a staggering investment of up to $100 billion. The deal aims to deploy 10 gigawatts of Nvidia-powered AI data centers, marking one of the largest infrastructure commitments in tech history.

The partnership was formalized through a letter of intent signed by both companies, with the first gigawatt of computing power scheduled to go live in the second half of 2026 on Nvidia’s upcoming Vera Rubin platform. This deployment will involve millions of GPUs and is expected to support OpenAI’s next-generation models, including its push toward artificial general intelligence (AGI).

Everything starts with compute, said OpenAI CEO Sam Altman. Compute infrastructure will be the basis for the economy of the future, and we will utilize what we’re building with Nvidia to both create new AI breakthroughs and empower people and businesses with them at scale.

The deal is structured as two intertwined transactions: Nvidia will invest in OpenAI for non-voting shares, while OpenAI will use the capital to purchase Nvidia’s chips. The first tranche of $10 billion will be deployed once OpenAI finalizes its purchase agreement for Nvidia systems.

Nvidia CEO Jensen Huang described the partnership as “monumental in size,” noting that the 10 GW deployment is equivalent to 4–5 million GPUs—roughly double the company’s annual output. “This investment and infrastructure partnership mark the next leap forward—deploying 10 gigawatts to power the next era of intelligence,” Huang said.

The announcement sent Nvidia’s stock soaring by 4.4%, adding nearly $170 billion to its market cap. Oracle, which is collaborating with OpenAI, Microsoft, and SoftBank on the $500 billion Stargate AI data center initiative, also saw a 6% bump.

Despite the excitement, analysts have raised concerns about the circular nature of the deal, with Nvidia’s investment potentially returning to the company via chip purchases. However, both firms emphasized the strategic value of co-optimizing hardware and software roadmaps to accelerate AI development.

OpenAI, which now boasts over 700 million weekly active users, will treat Nvidia as its preferred compute and networking partner. The collaboration complements existing alliances with Microsoft, Oracle, and SoftBank, and does not alter OpenAI’s ongoing efforts to develop its own custom AI chips.

As competition intensifies among tech giants like Google, Amazon, Meta, and xAI, this Nvidia–OpenAI alliance signals a new phase in the race to build scalable, high-performance AI infrastructure.

NVIDIA Launches $2.5B UK AI Investment Drive

NVIDIA has officially announced a landmark £2 billion investment to supercharge the United Kingdom’s AI startup ecosystem — a move hailed as a “major vote of confidence” in the UK’s tech future.

Key Highlights of the Investment

• Purpose: Catalyze globally transformative AI businesses via capital, compute, and talent.
• Target Regions: London, Oxford, Cambridge, and Manchester as AI growth zones.
• Strategic Partners: Accel, Air Street Capital, Balderton Capital, Hoxton Ventures, Phoenix Court.
• Beneficiary Startups: Wayve, Nscale, Revolut, Synthesia, PolyAI, Latent Labs, Basecamp Research.

Major Investments

Startup	Sector	Investment
Wayve	Autonomous Driving	£500 million
Nscale	AI Data Centers	£500 million
Others (Revolut, Synthesia, etc.)	Fintech, Generative AI, Bioinformatics	Undisclosed

Why It Matters

• UK’s AI talent pool is strong but scaling has been hindered by infrastructure and funding gaps.
• NVIDIA aims to democratize access to supercomputing and connect VCs with academic hubs.
• Energy costs and regional VC concentration have slowed growth — this investment addresses both.

Leadership Voices

“This is the age of AI — the big bang of a new industrial revolution.” — Jensen Huang, CEO of NVIDIA
“This partnership will create jobs, spark new industries and ensure the UK remains at the forefront of global AI leadership.” — UK Prime Minister Sir Keir Starmer

Global Context

• Microsoft: £22 billion commitment to UK AI infrastructure.
• OpenAI: Launch of Stargate UK supercomputing initiative.
• Blackstone: £100 billion pledge over 10 years for UK tech and infrastructure.

Nvidia Near $4 Trillion Market Valuation

Market Cap Peak of Nvidia hit $3.92 trillion on Thursday, momentarily surpassing Apple’s record of $3.915 trillion set in December 2024. It closed slightly lower at $3.89 trillion, still ahead of Microsoft ($3.7T) and Apple ($3.19T). The Growth Trajectory of Nvidia has increased exponentially, as its valuation has increased nearly 8x since 2021, when it was worth around $500 billion.

🧠 Why the Surge?

• AI Dominance: Nvidia’s chips, especially the H100 and successors, are the backbone of AI infrastructure—powering everything from large language models to autonomous systems.
• Client Base: Major tech firms like Microsoft, Amazon, Meta, Alphabet, and Tesla are racing to build AI data centers, fueling demand for Nvidia’s processors.
• Investor Confidence: Despite global trade tensions and competition from cheaper AI models (like China’s DeepSeek), Nvidia’s earnings and demand have remained robust.

📊 Market Impact

• S&P 500 Influence: Nvidia now makes up 7.4% of the S&P 500, and together with Microsoft, Apple, Amazon, and Alphabet, these five account for 28% of the index.
• Valuation Metrics: Nvidia trades at 32x forward earnings, below its 5-year average of 41, suggesting earnings are keeping pace with stock growth.

🧭 What’s Next?

• $4 Trillion Club: Analysts believe Nvidia and Microsoft could both officially cross the $4 trillion mark this summer, with eyes already on the $5 trillion milestone in the next 18 months.
• Risks: Heavy reliance on Taiwan’s TSMC for manufacturing and potential shifts in AI spending remain key vulnerabilities. 

📈 Nvidia Market Cap Timeline (1999–2025)

Year	Market Cap	Growth Highlights
1999	$0.96B	IPO era, early GPU development
2005	$6.25B	Rise of PC gaming and CUDA architecture
2015	$17.73B	Entry into deep learning and AI
2020	$323.24B	Pandemic-driven demand for GPUs and data centers
2021	$735.27B	AI boom begins, data center revenue surges
2023	$1.22T	Generative AI explosion (ChatGPT, LLMs)
2024	$3.35T	Surpasses Amazon and Alphabet
2025	$3.83T	Briefly becomes world’s most valuable company

Schneider Electric and NVIDIA Team Up to Power €200 Bn AI Revolution in Europe

• R&D initiatives underscore companies’ commitment to co-developing new cooling, power, building management and control systems for digital and physical AI data centers
• Schneider Electric announces launch of new NVIDIA-enabled rack solution

Schneider Electric, the leader in the digital transformation of energy management and automation, today announced it is collaborating with NVIDIA to serve the growing demand for sustainable, AI-ready infrastructure. Together, Schneider Electric and NVIDIA are advancing research and development (R&D) initiatives for power, cooling, controls, and high-density rack systems to enable the next generation of AI factories across Europe and beyond.

This unique global partnership, announced during NVIDIA GTC Paris, brings together the world leaders in sustainability and accelerated computing to support the European Union’s AI infrastructure ambitions and its “InvestAI” initiative, which plans to mobilize a €200 billion investment in AI.

Leveraging its expertise in AI-ready infrastructure, sustainability, and grid coordination, Schneider Electric and NVIDIA are together responding to the European Commission’s “AI Continent Action Plan,” which outlines a shared mission to set up at least 13 AI factories across Europe, while establishing up to five AI gigafactories.

“Schneider Electric and NVIDIA are not just partners — our teams are driving advanced R&D, co-developing the infrastructure needed to power the next wave of AI factories globally,” said Olivier Blum, CEO of Schneider Electric. “Together, we’ve seen tremendous success in deploying next-generation power and liquid cooling solutions, purpose-built for AI data centers. This strategic partnership — bringing together the world leaders in sustainability and accelerated computing — allows us to further accelerate this momentum, pushing the boundaries of what’s possible for the AI workloads of tomorrow.”

“AI is the defining technology of our time—the most transformative force reshaping our world,” said Jensen Huang, founder and CEO, NVIDIA. “Together with Schneider Electric, we are building AI factories: the essential infrastructure that brings AI to every company, industry, and society.”

New NVIDIA-Enabled Infrastructure Solutions

In support of today’s announcement, Schneider Electric has also unveiled a suite of AI-ready data center solutions, including new EcoStruxure™ Pod and Rack Infrastructure. Designed to accelerate AI developments globally, the Prefabricated Modular EcoStruxure Pod Data Center is a scalable, pod-based architecture, enabling rapid AI data center deployment.

As part of this, a new Schneider Electric Open Compute Project (OCP) inspired rack system has also been developed to support the NVIDIA GB200 NVL72 platform that uses the NVIDIA MGX modular architecture, integrating Schneider Electric into NVIDIA HGX and MGX ecosystems for the first time.

These announcements build on a series of milestones shared by the two global leaders earlier this year, including Schneider Electric and ETAP unveiling the world’s first digital twin for electrical and large-scale power systems in AI factories using the NVIDIA Omniverse Blueprint.

Together, Schneider Electric and NVIDIA have also co-developed a series of full electrical and liquid cooling-based reference designs as an approved CDU vendor for NVIDIA — many of which also include solutions from Motivair’s liquid cooling portfolio, following its acquisition by Schneider Electric in March 2025.

Through this expanded and deepened strategic partnership, Schneider Electric and NVIDIA will continue to accelerate their infrastructure initiatives, fast-tracking new product rollouts and reference designs to build the AI factories of the future.

Alphabet Spinout SandboxAQ, Backed by Nvidia, Unveils Synthetic Molecule Megaset to Revolutionize Drug Discovery

SandboxAQ, an AI startup spun out of Google parent Alphabet and backed by Nvidia, has announced that it has released a massive dataset of 5.2 million synthetic 3D molecules to accelerate drug discovery. These molecules don’t exist in nature—they were generated using Nvidia’s chips and grounded in real-world experimental data to simulate how drugs bind to proteins, a critical step in developing effective treatments.

This synthetic dataset, called the Structurally Augmented IC50 Repository (SAIR), is publicly available and designed to train AI models that can predict drug-protein interactions far faster than traditional lab methods. The goal? To virtually replicate lab results with high accuracy, potentially compressing months of research into a single AI-driven prediction.

It’s a bold move that blends physics-based modeling with machine learning—an approach that could reshape how we think about early-stage pharmaceutical R&D. And with SandboxAQ planning to monetize its own trained models, it’s also a glimpse into the future of AI-powered biotech platforms.

While the dataset is public, SandboxAQ plans to monetize its proprietary AI models trained on it—essentially offering virtual labs as a service.

Headquartered in Palo Alto, California, right in the heart of Silicon Valley, SandboxAQ is a fascinating fusion of quantum tech and AI muscle. It emerged from Alphabet’s moonshot factory and is backed by Nvidia, with nearly $1 billion in venture capital fueling its ambitions.

SandboxAQ's signature tech is Large Quantitative Models, the AI systems grounded in the laws of physics, chemistry, and biology. These aren’t just data-driven models; they simulate the real world with scientific rigor. Think of it as a tireless digital researcher. This tool autonomously explores millions of chemical pathways, helping discover novel molecules far beyond human capacity.

Beyond drug discovery, SandboxAQ is tackling – 1) Cybersecurity: Detecting cryptographic vulnerabilities; 2) Navigation: Enhancing precision in GPS-denied environments. 3) Medical Diagnostics: Using AI to analyze cardiac signals. 4)Materials Science: Predicting atomic-level properties for breakthrough materials.

SandboxAQ was founded by Jack Hidary, a tech entrepreneur with a deep background in quantum computing and neuroscience. He previously led a quantum initiative at Alphabet before spinning it out as SandboxAQ in 2022. Hidary studied philosophy and neuroscience at Columbia University and has authored a well-regarded book on quantum computing.

While Hidary is the primary founder and CEO, the company’s early development was also shaped by influential figures like Eric Schmidt, former CEO of Google, who serves as Chairman of the Board.

Nvidia Faces $5.5 Billion Hit as U.S. Tightens AI Chip Export Rules to China

Nvidia is facing a $5.5 billion charge after the U.S. government restricted exports of its H20 Al chips to China. Nvidia's shares dropped about 6% following the announcement. 

The H20 was designed to comply with earlier export limits, but officials now fear it could be used in Chinese supercomputers, prompting indefinite licensing requirements.

China previously accounted for 20% of Nvidia's revenue, but this has now shrunk to about 10%, with expectations that it could drop to near zero.

This move is part of Washington's broader strategy to limit China's access to advanced Al hardware, escalating tensions in the global tech race. Nvidia's stock dropped about 6% following the announcement.

The H20 was Nvidia's most advanced chip available in China, widely used by companies like Tencent, Alibaba, and ByteDance. These firms had ramped up orders due to growing demand for Al models.

While the H20 has lower computing capabilities than Nvidia's top-tier chips, its high-speed memory and connectivity raised concerns that it could be used in Chinese supercomputers, prompting the U.S. to impose indefinite licensing requirements.

Meanwhile, Nvidia is pivoting towards its Blackwell-series Al chips, which are expected to be the next major product line. Besides, the company has recently announced plans to build AI servers worth up to $500 billion in the U.S. over the next four years, aligning with efforts to boost domestic tech infrastructure.

Nvidia is bracing for additional U.S. export controls under proposed "AI diffusion rules," which could further limit its ability to sell advanced AI hardware globally. Revenue from China has halved compared to pre-restriction levels, with Huawei emerging as a key competitor.

Analysts predict that Chinese firms may pivot to Huawei or other domestic alternatives, accelerating China’s push for semiconductor independence.

The U.S. government now requires indefinite export licenses for H20 shipments to China, citing concerns over potential use in Chinese supercomputers.

HCLTech integrates NVIDIA AI Enterprise and Omniverse with HCLTech’s GenAI solutions

HCLTech, a leading global technology company, announced that it has integrated NVIDIA AI Enterprise with its GenAI-led service transformation platform, AI Force, and NVIDIA Omniverse with its physical AI solution, SmartTwin™. These integrations aim to drive faster AI adoption for enterprises by streamlining software development cycles and enhancing engineering efficiency.

The NVIDIA AI Enterprise software—including NVIDIA NIM and NeMo Retriever microservices —along with the NVIDIA Llama Nemotron model family and NVIDIA AI Blueprints, will enable HCLTech’s AI Force enterprise users to achieve accelerated release timelines, improved code quality and enhanced operational efficiency across coding, testing, legacy modernization, and process optimization.

Simultaneously, HCLTech’s SmartTwin™ platform will harness NVIDIA Omniverse™, enabling enterprises to build interoperable data pipelines on OpenUSD, integrate third-party engineering tools and run high-fidelity virtual simulations. The result: faster, more successful product launches and significant cost savings through optimized processes and reduced reliance on physical prototypes.

"AI is empowering enterprises to achieve operational excellence and business growth across industries. Our work with NVIDIA will bring a wide range of capabilities and benefits to businesses across industries as they adopt AI products and services across their operations," said Vijay Guntur, CTO and head of Ecosystems at HCLTech.

"Agentic and physical AI are transforming every industry from customer service and healthcare to manufacturing and logistics by automating complex workflows, optimizing operations and fostering sustainability and growth,” said John Fanelli, Vice President, Enterprise Software at NVIDIA. "With the integration of the NVIDIA AI Enterprise and NVIDIA Omniverse technologies, HCLTech's AI Force and SmartTwin platforms can help businesses rapidly integrate AI and simulation technology into their operations."

IBM Teams Up with NVIDIA to Supercharge AI Development on the Cloud

IBM has announced a collaboration with NVIDIA to enhance AI capabilities at scale. This partnership focuses on integrating NVIDIA's AI Data Platform technologies with IBM's offerings, such as IBM Fusion and watsonx.

Key highlights of this collaboration include:

• Content-Aware Storage (CAS): IBM plans to introduce CAS in its hybrid cloud infrastructure, enabling enterprises to process unstructured data more effectively for AI applications like retrieval-augmented generation (RAG) and reasoning.
• Enhanced AI Accessibility: IBM aims to integrate its watsonx platform with NVIDIA's technologies, allowing organizations to develop and deploy AI models across various cloud environments.
• Support for Compute-Intensive Workloads: IBM Cloud will expand its NVIDIA accelerated computing portfolio, including the availability of NVIDIA H200 Tensor Core GPU instances, designed for high-performance AI workloads.

This collaboration is expected to drive innovation in generative AI and agentic AI applications.

A 2024 IBM report found that more than three in four executives surveyed (77 percent) say generative AI is market-ready, up from just 36 percent in 2023. With this push to put AI into production comes an increased need for compute and data-intensive technologies. The collaboration between IBM and NVIDIA will enable IBM to provide hybrid AI solutions that take advantage of open technologies and platforms while also supporting data management, performance, security, and governance.

"IBM is focused on helping enterprises build and deploy effective AI models and scale with speed," said Hillery Hunter, CTO and General Manager of Innovation, IBM Infrastructure. "Together, IBM and NVIDIA are collaborating to create and offer the solutions, services and technology to unlock, accelerate, and protect data – ultimately helping clients overcome AI's hidden costs and technical hurdles to monetize AI and drive real business outcomes."

"AI agents need to rapidly access, fetch and process data at scale, and today, these steps occur in separate silos," said Rob Davis, vice president, Storage Networking Technology, NVIDIA. "The integration of IBM's content-aware storage with NVIDIA AI orchestrates data and compute across an optimized network fabric to overcome silos with an intelligent, scalable system that drives near real-time inference for responsive AI reasoning."

To learn more about IBM's presence at GTC, please visit https://www.nvidia.com/gtc/session-catalog/?search.suggestedaudiencelevel=1732117107498003nOoA&search=ibm#/

OpenAI Signs $12 Bn Deal with NVIDIA-backed CoreWeave

OpenAI has reportedly signed a significant five-year deal worth $11.9 billion with CoreWeave, a cloud services provider specializing in AI-focused GPU infrastructure and backed by NVIDIA.

As part of this agreement, OpenAI will receive $350 million worth of equity in CoreWeave. This partnership comes ahead of CoreWeave's planned Initial Public Offering (IPO) and represents a major development for the company, which has seen rapid growth in recent years.

CoreWeave, which began as a cryptocurrency mining operation, has evolved into a leading player in the AI cloud service industry, operating an AI-specific cloud infrastructure across 32 data centers with over 250,000 NVIDIA GPUs. The deal with OpenAI is expected to reduce CoreWeave's dependence on Microsoft, which accounted for 62% of its revenue in 2024.

This partnership comes ahead of CoreWeave's planned Initial Public Offering (IPO) and represents a major development for the company, which has seen rapid growth in recent years.

This strategic move highlights the changing dynamics between Microsoft and OpenAI, as the latter seeks more computing resources amid claims of being "out of GPUs". The partnership with CoreWeave is expected to provide a major boost to the company ahead of its IPO.

According to a report by The Information, It’s unclear whether the OpenAI contract is a net new deal for CoreWeave. Microsoft has previously signed deals with CoreWeave to get extra capacity for OpenAI. Notably, Microsoft is CoreWeave’s largest customer and has signed deals to spend more than $10 billion renting AI servers through 2030.

The deal with OpenAI is expected to reduce CoreWeave's dependence on Microsoft, which accounted for 62% of its revenue in 2024.

CoreWeave has raised more than $14.5 billion in debt and equity across 12 financing rounds. Last year, CoreWeave raised over $7 billion in one of the largest private debt financing rounds in history, led by asset managers Blackstone and Magnetar.

Reliance Buys Advance NVIDIA AI Chips for World's Largest Data Centre - Report

According to a Bloomberg report, Mukesh Ambani's Reliance Industries is planning to build what could become the world's largest data centre in Jamnagar, Gujarat. The facility is expected to have a capacity of 3 GW, significantly surpassing the current largest data centre, Microsoft's 600-megawatt site in Virginia. The project could cost between $20 billion and $30 billion.

Reliance Industries Limited is procuring advanced AI semiconductors from Nvidia to support this ambitious project. These high-performance chips are essential for complex computations required by AI-driven tools.

The data centre will be powered primarily by renewable energy sources, including solar, wind, and hydrogen power. This initiative aligns with Reliance's strategy to boost India's AI capabilities and make AI more affordable and accessible.

This ambitious project aims to significantly boost India's artificial intelligence capabilities.

The facility is expected to have a total capacity of 3 GW, which is three times larger than the largest existing data centres.

As of now, the largest data centre in the world is the China Telecom-Inner Mongolia Information Park in Hohhot, Inner Mongolia, China. This massive facility spans 10.7 million square feet and consumes 150 megawatts of power. It includes not only data storage and processing infrastructure but also contact centers, warehouses, offices, and housing quarters.

Cost and Investment

Building such a massive facility could require an investment of $20 to $30 billion. Reliance Industries has about $26 billion in cash reserves, but funding such a project would still be a challenge.

Ambani aims to lower the cost of AI inferencing, making AI more affordable and accessible to businesses and startups in India.

"AI inferencing" is the process by which a trained machine learning model applies its learned patterns to new, unseen data to make predictions or decisions. For example, an AI model trained to recognize spam emails can infer whether a new email is spam or not based on the patterns it learned during training.

AI inferencing requires significant computational power, often involving GPUs or specialized hardware like TPUs. The cost of using these resources can be substantial, especially for large-scale models.

For example, companies using NVIDIA's AI inference platform can achieve significant cost savings through optimized performance and energy efficiency. Similarly, startups like Pipeshift offer modular inference engines that can reduce GPU usage by up to 75%, leading to substantial cost savings.

This initiative aligns with Reliance's strategy to disrupt the market, similar to how Reliance Jio revolutionized the telecom sector by offering affordable services. The cost of using these resources can be substantial, especially for large-scale models.

This announcement comes within few days after the announcement of The Stargate Project, a massive artificial intelligence initiative, announced by OpenAI, SoftBank, Oracle, and MGX. The project aims to invest $500 billion over the next four years to build AI infrastructure in the United States. Masayoshi Son, the founder of SoftBank, will chair the project, while Sam Altman from OpenAI will manage operations..

Nvidia Unveils Desktop Sized Personal AI Supercomputer Called Digits

Nvidia recently unveiled Project DIGITS, a personal AI supercomputer designed to bring cutting-edge AI computing to researchers, data scientists, and students.

Project DIGITS features the new NVIDIA GB10 Grace Blackwell Superchip, offering a petaflop of AI computing performance for prototyping, fine-tuning and running large AI models.

With Project DIGITS, users can develop and run inference on models using their own desktop system, then seamlessly deploy the models on accelerated cloud or data center infrastructure.

Here are some key details:

Key Features:

• GB10 Grace Blackwell Superchip: At the heart of Project DIGITS is the GB10 Superchip, delivering up to 1 petaflop of AI performance.
• Unified Memory: 128GB of unified memory and up to 4TB of NVMe storage for handling large datasets.
• Power Efficiency: Developed in collaboration with MediaTek, the superchip offers industry-leading efficiency.
• Scalability: Two DIGITS systems can be connected to handle models up to 405 billion parameters.
• Software Stack: Preloaded with NVIDIA's AI software stack, including tools like NVIDIA NeMo for fine-tuning models and NVIDIA RAPIDS for accelerating data science tasks.

Availability:

Price: Starting at $3,000, Project DIGITS will be available from Nvidia and its partners starting in May 2025.

Usage: Designed for prototyping, fine-tuning, and running AI models locally, with seamless deployment to cloud or data center environments.

This innovation aims to make powerful AI computing accessible to a broader audience, empowering developers to engage and shape the age of AI.