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Year: 2019

Five Things You Always Wanted to Know About AI, But Weren’t Afraid to Ask

December 31, 2019 by
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Looking for inferencing in the real world? Turn on your smartphone.

They say there’s no such thing as a dumb question. As someone who asks dumb questions for a living, I can tell you that’s a really stupid thing to say.

But the best questions are often the ones where someone smart explains something from the ground up to a total novice (read: me). The beauty of NVIDIA: there are a lot of smart people upon whom I can inflict my very dumbest questions.

Turns out I’m not alone. Month in and month out, tens of thousands of readers ask search engines these very questions. And they get connected to the answers through our blog.

What are they? Smart question. Here are five of our most popular in 2019.

What’s the Difference Between a CPU and a GPU?

This post is over a decade old, but the answer — thanks to the emergence of deep-learning driven AI, supercomputing, and self-driving cars — is more relevant than ever. That’s why we’ve updated our original post earlier this year, and why more readers are seeing this post than ever.

What’s the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?

Visualize the fields of AI, machine learning and deep learning as concentric circles. AI — the idea that came first — is the largest circle. Then comes machine learning, which blossomed later. And finally deep learning — which is driving today’s AI explosion — fitting inside both. Click on the link, above, for more.

What’s the Difference Between Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning?

This is one of the key questions in AI right now, which is why this post has become one of our most popular. Click on the link for a plain English answer to these questions, and a walk through the kinds of datasets and problems that lend themselves to each kind of learning.

What’s the Difference Between Deep Learning Training and Inference?

This is another question that’s drawn more readers over time. Training, in short, is the process of running data through a neural network to teach it a task. That’s taught computers to do things that, just a decade ago, most believed could only be done by humans. Inference, by contrast, is the process of putting that trained network to work, in everything from hyperscale data centers to autonomous machines.

What’s the Difference Between Ray Tracing and Rasterization?

Used to be if you wanted to see ray tracing, you went to the movies. If you wanted to see rasterization, you fired up a video game. Ray tracing models the way light moves around the real world beautifully but it’s computationally intensive. Rasterization, by contrast, can be done in a hurry. NVIDIA’s latest Turing architecture GPUs blur these lines, with hardware acceleration for real-time ray tracing, making truly cinematic games possible.

Have a question you want answered? Send us your idea.

 

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Why Deploying Physical AI at Scale Demands Safety at Every Layer

As autonomous vehicles, robots and other AI-driven machines take on more complex tasks in the physical world, safety must extend across every layer of the system.
September 21, 2026 by
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Physical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As these machines enter roads, factories, warehouses and other environments shared with people, safety must scale with them.

Physical AI safety means proving that AI-driven machines — AVs, humanoid robots, industrial robots and more — behave safely when their decisions turn into physical action. That requires safety across the hardware, software, AI, operating environment and deployment lifecycle — not a one-time check before deployment.

Why Is Safety the Key to Scaling Physical AI?

After years of testing and benchmarking, AVs continue to expand commercially. That progress has required developers to demonstrate how automated systems address potential hardware and software failures, limitations in intended functionality and AI-specific risks.

Robotics is approaching a similar inflection point as autonomous machines move into factories, warehouses and other environments shared with people.

Across physical AI, manufacturers, regulators, insurers and workplace safety teams need evidence that hardware, software, AI behavior and operating environments can work together safely without human intervention.

Why Does Physical AI Need a New Safety Model?

Four shifts define new safety standards:

  • Dynamic environments require context-aware safety. Roads, factories and warehouses cannot be fully controlled through static zones or physical barriers. Autonomous systems must perceive changing conditions, adapt their behavior and reach a safe state when something unexpected occurs.
  • AI behavior requires its own assurance. Testing must assess AI software alongside traditional functional safety, using design-time, runtime and validation-time guardrails. Emerging standards such as ISO/IEC TS 22440 are beginning to address these AI-specific risks.
  • Deployment is ongoing. AVs and robots evolve through software and model updates, new tasks and changing operating conditions. Material changes may require additional safety testing.
  • Validation at scale requires simulation and synthetic data. The number and complexity of potential scenarios requires real-world testing to be combined with simulation, synthetic data generation and scenario reconstruction.

Together, these shifts require safety to be operationalized across design, deployment and validation, from the underlying hardware to AI behavior and the operating environment. 

What Safety Foundation Has NVIDIA Built for Physical AI?

Physical AI safety requires specialized engineering, data, processes and validation that few companies can reproduce alone. NVIDIA’s safety foundation draws on more than a decade of development in AV safety, building expertise in functional safety, sensor fusion, AI behavior assurance, vision AI, simulation and real-world validation.

NVIDIA Halos is the first and only full-stack safety system for physical AI, helping developers engineer safety across every layer of design, validation and deployment. The principles are shared across AVs and robotics, while the platforms, standards and evidence remain specific to each domain.

For AV development, Halos spans:

  • Hardware: NVIDIA DRIVE AGX Thor provides safety-engineered accelerated compute, while NVIDIA Hyperion provides the full-stack vehicle platform and reference architecture for level 4 AVs.
  • Operating system and middleware: Halos OS provides a unified software foundation built on ASIL-D certified DriveOS. Halos Core and Halos Middleware support system isolation, monitoring and deterministic communication.
  • End-to-end model: NVIDIA Alpamayo offers open reasoning vision language action models that bring explainability to long-tail scenarios.
  • Simulation and validation: The NVIDIA Halos Safety Evaluation Framework provides tools and guidelines for generating evidence to support AV safety cases across different levels of automation.

Together, these elements connect cloud-based AI development and simulation with in-vehicle deployment so safety evidence can remain traceable across the vehicle lifecycle.

For robotics, Halos spans:

  • Hardware: NVIDIA IGX Thor is an industrial-grade module that combines accelerated computing and functional safety on one platform with a dedicated Functional Safety Island. It’s designed to support systems developed for standards including IEC 61508 and ISO 13849.
  • Software: Halos Core for IGX provides the software foundation for safety-related operating functions, including fault detection,  monitoring and reporting, along with the communication and processing capabilities that connects sensors, actuators and other safety components 
  • Real-time sensing: NVIDIA Holoscan Sensor Bridge connects sensor data with AI and safety-related processing, helping systems identify invalid information and execute defined safety responses.
  • Simulation and validation: NVIDIA Isaac Lab and NVIDIA Omniverse libraries let developers test robot behavior across relevant conditions and edge cases, complementing real-world validation.
  • Outside-in safety: The open source NVIDIA Halos Outside-In Safety Blueprint uses external cameras and vision AI agents to extend awareness beyond onboard sensors and support facility-level monitoring and functional safety use cases.

Across both AV and robotics, the NVIDIA Halos AI Systems Inspection Lab turns safety, cybersecurity and AI safety requirements into repeatable inspections and helps prepare Halos integrations for final system-level certification by third-party agencies.

Who Is Building With the NVIDIA Halos Safety Ecosystem?

NVIDIA Halos connects the companies that build, integrate, assess and deploy physical AI solutions, including product developers, software and embedded-system providers, sensor and silicon companies, safety solution developers and certification bodies.

In autonomous vehicles, Geely, Isuzu, Nissan (powered by Wayve software) and Einride are building level 4-ready vehicles on NVIDIA Hyperion, supported by Halos OS.

Uber, Grab, Lyft and other mobility providers are also using Hyperion to scale robotaxi development and deployment. Members of the NVIDIA Halos AI Systems Inspection Lab include AUMOVIO, Bosch, Gatik, Hesai, Lucid, MIRA, onsemi, PlusAI, Sony, Valeo and Wayve, spanning autonomous-driving development, ADAS, sensors, silicon, systems integration, validation and safety assurance.

In robotics, acontis and QNX provide the embedded software needed to run safety functions predictably, while Advantech and NexCOBOT build safety-designed NVIDIA IGX systems. Infineon, NXP, STMicroelectronics and Texas Instruments contribute sensor, safety-microcontroller and other semiconductor technologies. KION Group is developing functional safety agents for autonomous forklifts. Agility is integrating NVIDIA IGX Thor and Halos Core into the safety system for its Digit 5 humanoid.

How Is NVIDIA Halos Independently Assessed?

For AVs, TÜV SÜD certified NVIDIA’s Automotive Product Lifecycle software process and DriveOS 6.0 to ISO 26262 ASIL D, as well as NVIDIA’s automotive engineering processes to ISO/SAE 21434. TÜV Rheinland also performed an independent UNECE safety assessment of NVIDIA DRIVE AV.

For robotics, TÜV Rheinland is inspecting NVIDIA IGX Thor, Halos OS and Holoscan Sensor Bridge for functional-safety certification readiness, building on TÜV SÜD’s inspection of the Thor SoC and Halos Core for ISO 26262.

Across physical AI, ANAB has accredited the NVIDIA Halos AI Systems Inspection Lab as an ISO/IEC 17020 inspection body. The lab inspects scoped Halos integrations and helps companies prepare for final certification by independent third-party bodies.

The companies that scale physical AI will not simply build the most capable systems. They will build systems that can be assessed, certified, deployed and trusted in the real world. Designing functional safety from the start is what separates a prototype from a scalable solution.

Learn more about NVIDIA Halos for AVs and robotics, and explore the full-stack safety architecture for physical AI.

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NVIDIA Alpamayo 2 Super, the Frontier Open Model for Robotaxis and Autonomous Vehicles, Now Available for Commercial Use

Open commercial licensing, benchmark‑leading reasoning and inspectable decisions bring autonomous vehicles, including robotaxis, closer to production and widescale deployment.
August 4, 2026 by
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For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for.

Handling these long‑tail events takes more than just object detection and motion prediction. AVs must understand the situation, reason about cause and effect, choose the right action and turn that decision into a safe, comfortable path — all in real time and in a way developers can inspect, validate and trust.

NVIDIA Alpamayo 2 Super, available now for commercial use, is part of the Alpamayo family, the most-adopted open reasoning models for autonomous driving on Hugging Face, supporting a wide range of AV-relevant capabilities within a single foundation model. 

Built on NVIDIA Cosmos 3 Super Reasoner and post‑trained with reinforcement learning, the model advances the AV ecosystem on two fronts: open commercial licensing and leading multitask capabilities for autonomous driving. 

Alpamayo 2 Super is part of NVIDIA’s growing collection of open models, datasets and tools for autonomous driving, expanding access, strengthening competition, giving developers greater control and supporting safer, more transparent AV deployment. 

Open Licensing for Production AVs

Alpamayo 2 Super is available on Hugging Face under OpenMDW‑1.1, the Linux Foundation’s permissive license for open AI model distributions. The license covers fine‑tuning, derivative models and commercial redistribution, allowing AV developers, automakers, truckmakers and suppliers to adapt Alpamayo to their own data, driving policies and deployment strategies. 

This openness lets AV researchers and companies keep control of their own data and infrastructure, as well as own the value they create through specialized models and accumulated know‑how. Such control is essential for workflows involving proprietary fleets and safety. 

Earlier Alpamayo releases were initially introduced for R&D. The OpenMDW license is now being  applied across the entire Alpamayo model family so developers can deploy any of the models commercially without requiring additional permissions. This creates a direct path from adaptation to deployment.

Open weights make that path economically viable. Teams can build on advanced reasoning without re‑training every foundation capability from scratch or paying frontier‑model costs for every task, matching the right model to the right job at the right cost. 

Alpamayo 2 Super enables frontier-scale reasoning in cloud-based development workflows, where developers can generate high-quality reasoning traces, synthetic training data and teacher outputs for model distillation. Within the Alpamayo model family, Alpamayo 2 Super delivers the highest reasoning and driving performance for multimodal autonomous driving development, while Alpamayo 1.5 and Alpamayo 1 provide more cost-efficient options for cloud-based development and model distillation.

The resulting distilled models can then be optimized for efficient, real-time inference in production vehicles. Together, the Alpamayo model family provides a cloud-to-car workflow that combines frontier-scale reasoning with scalable deployment across commercial AV fleets.

For AV programs, that means frontier‑scale reasoning in the cloud and efficient, specialized models in the vehicle — a more sustainable way to scale safe autonomy into commercial fleets.

Benchmark-Leading Reasoning at Frontier Scale

Alpamayo 2 Super ranks first on LingoQA, an autonomous driving reasoning benchmark, among nearly 40 models evaluated. In NVIDIA testing using the Lingo‑Judge metric, it outperformed Qwen2.5‑VL 72B by 17.0 points, Gemini 2.5 Pro by 15.1 points and GPT‑4o by 23.2 points, demonstrating state‑of‑the‑art reasoning for driving‑centric scenarios. Alpamayo 2 Super also ranks first across all autonomous driving benchmarks evaluated by NVIDIA, underscoring its leading performance across a broad range of AV capabilities. 

Alpamayo 2 Super offers 3x the scale of the 10‑billion‑parameter NVIDIA Alpamayo 1.5 and Alpamayo 1 models. The added capacity helps the model better generalize reasoning from sparse examples — a critical capability for the rare, multi‑agent interactions where conventional systems often struggle. 

The model reasons over full‑surround camera coverage, fusing views from the vehicle’s front, sides and rear. This 360‑degree context enables richer understanding of lane changes, merges, unprotected turns and complex intersections, where risks commonly arise.

A Multitask Foundation Model for Robotaxis and Autonomous Driving

For each driving situation, Alpamayo 2 Super can produce five tightly coupled outputs:

  • A trajectory describing the vehicle’s planned path.

  • A chain‑of‑causation (CoC) trace that explains the reasoning behind the decision.

  • A meta‑action (e.g., yield, lane changes, stops) that captures the model’s intent.

  • Reasoning auto-labels that generate CoC annotations for training and validation data.

  • Visual question answering responses with 2D visual grounding that link the model’s answers to specific regions in camera images.

Together, these outputs offer insight into the model’s decision-making process. Developers can tie what the model observed to the action it selected, making decisions easier to understand, critique and validate. 

CoC traces integrate with NVIDIA Halos safety‑validation workflows and support AI safety aligned with ISO/PAS 8800 requirements, providing a stronger foundation for AV safety engineering. 

Alpamayo 2 Super can also be deployed as an autolabeler to generate CoC labels and perform visual question answering with 2D grounding on proprietary fleet data. By linking its reasoning to specific regions in camera images, the model can transform raw driving clips into richer training data, compressing annotation cycles from months to days.

Beyond planning and auto-labeling, Alpamayo 2 Super supports scene understanding, model critiquing and knowledge distillation. These multitask capabilities enable developers to use a single foundation model across more of the development stack, simplifying tooling and accelerating iteration.

An Open Ecosystem for Reasoning‑Based AVs

Alpamayo 2 Super is part of a broader family of open models, frameworks and datasets for AV development. 

Other tools in the family include: 

  • NVIDIA AlpaSim, which provides closed‑loop simulation.

  • NVIDIA AlpaGym, which enables high‑throughput reinforcement learning.

  • NVIDIA Physical AI Open Datasets, which supply data for training and testing.

  • Open training recipes and an autolabeling pipeline to accelerate model development, training and validation.

Alpamayo has already surpassed 500,000 downloads on Hugging Face, reinforcing its position as the most-adopted open reasoning model family for autonomous driving on the platform.

Download NVIDIA Alpamayo 2 Super on Hugging Face to explore the model, evaluate its reasoning capabilities and start building the next generation of robotaxis and autonomous vehicles.

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For Robotaxis, Safety Must Be Built In, Not Bolted On

June 10, 2026 by
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Editor’s note: The name of NVIDIA DRIVE Hyperion was changed to NVIDIA Hyperion in September 2026. All references to the name have been updated in this blog.

A car pulls up to the curb. The app says, “Your ride is here.” No one’s in the driver’s seat. For people who live in one of the dozens of cities now hosting robotaxi services, this is already a reality.

The robotaxi industry has moved from prototype milestones to commercial operations, with an expanding ecosystem accelerating the pace of deployment. New collaborations announced at NVIDIA GTC Taipei reflect robotaxi programs spinning up around the world:

  • Uber and Autobrains are launching a robotaxi program in Munich on the NVIDIA Hyperion platform, using Autobrains’ agentic AI to support scalable operations. 
  • Foxconn is expanding its collaboration with NVIDIA to deploy robotaxi fleets, combining its services with NVIDIA Hyperion for rapid integration and scaling in Taiwan.
  • VinFast is working with Autobrains to bring level 4 vehicles built on Hyperion to the Southeast Asia market.
  • HUMAIN is working to bring Hyperion-powered robotaxis to Saudi Arabia, expanding the platform’s global footprint into the Middle East.

Building a Safe Software Foundation

As the robotaxi industry scales, safety is paramount.

Regulators, certification bodies and developers are scrutinizing what safe deployment at scale requires. 

Industry discussion on level 4 autonomy often centers on what the vehicle can perceive and decide. 

That discussion is well-founded. Accurate perception, sound decision-making and handling the unexpected are difficult problems, and real progress toward solving them is being made.

But perception and decisions alone are not the whole story. Regulators require something more: proof that the overall system behaves reliably, isolates faults before they escalate and never operates outside the boundaries it was designed for. 

Robotaxi safety requires solving four distinct challenges simultaneously:

  • A safety-certifiable operating system
  • Safe, standardized hardware and software interfaces
  • AI that operates within verifiable guardrails
  • Validation at scale before vehicles touch public roads

To help solve these challenges, the recently introduced Halos Operating System (OS) — a component of the NVIDIA Halos full-stack, comprehensive safety system — offers a unified, production-ready safety foundation for AI-driven vehicles, built on NVIDIA Hyperion. It comprises: 

Halos Core: A Certified OS Foundation

At the foundation of NVIDIA Halos OS is Halos Core, which is the next generation of NVIDIA DriveOS and certified to automotive safety standards. It’s audited, documented and proven to behave predictably under fault conditions, with a hypervisor — a specialized software layer — that isolates safety-critical functions so failures can’t reach vehicle controls. 

Halos Core is compliant with ISO 26262 ASIL D, includes safety-certified support for NVIDIA CUDA and TensorRT, and provides the TensorRT Edge-LLM open source framework for high-performance large language model inference.

Halos SDK: Standardized and Safe Interfaces

A robotaxi integrates cameras, radar, lidar and other sensors, each streaming data in a different format at a different rate. Without a standardized middleware layer, every hardware change forces teams to manually rebuild those integrations. 

Halos SDK removes that burden. Its sensor abstraction layer decouples the autonomous driving stack from individual sensor drivers, so adding or swapping a sensor no longer causes ripples through application code, while a vehicle abstraction layer connects the autonomous driving stack to the rest of the vehicle through a single, consistent interface. 

On top, Halos SDK provides the runtime building blocks that safety-critical software demands: a deterministic application-level scheduler for predictable timing, zero-copy inter-process communication that moves data without added latency, a comprehensive system error-handling framework and a robust scenario data recorder — delivering the foundation for highly reliable and low-latency automotive applications.      

Halos Applications: Safety Guardrails for AI

AI models can match human driving behavior, but regulators require more than performance. 

The Halos Applications layer provides safety guardrails for AI through deterministic, rule-based functions, analyzed and designed to behave within defined bounds. It includes world model perception and the top-rated NVIDIA DRIVE active safety stack featuring automatic emergency braking, lane departure warning, blind spot monitoring, collision warning and more. 

In addition, in Halos Applications, Halos OS can be combined with end-to-end AI models for which explainability and transparency are essential. This includes the NVIDIA Alpamayo family of open models for autonomous vehicle development, which enables chain-of-thought reasoning, continuously evaluating the road, planning next steps and adapting to changing conditions.

The Halos Safety Evaluation Framework

Halos Infra is the cloud-side development infrastructure that enables autonomous vehicle training, simulation and validation at scale. It’s the foundation for the recently released NVIDIA Halos Safety Evaluation Framework (SEF).

SEF provides the tools and guidelines needed to build a credible safety case, from L2 driver assistance to L4 robotaxis. It draws on more than 330 research papers and 1,000 patents developed within NVIDIA Halos OS.

Halos Infra runs on NVIDIA’s three-computer autonomous driving solution: 

Halos OS spans the full development lifecycle — from training and simulation in Halos Infra to inference in the vehicle itself.

Learn more about NVIDIA Halos.

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