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NVIDIA and Zoox Pave the Way for Autonomous Ride-Hailing

‘The world has never seen a robotics company like this before,’ NVIDIA founder and CEO Jensen Huang said in a fireside chat with Zoox CEO Aicha Evans and Zoox cofounder and CTO Jesse Levinson.
July 31, 2024 by
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In celebration of Zoox’s 10th anniversary, NVIDIA founder and CEO Jensen Huang recently joined the robotaxi company’s CEO, Aicha Evans, and its cofounder and CTO, Jesse Levinson, to discuss the latest in autonomous vehicle (AV) innovation and experience a ride in the Zoox robotaxi.

In a fireside chat at Zoox’s headquarters in Foster City, Calif., the trio reflected on the two companies’ decade of collaboration. Evans and Levinson highlighted how Zoox pioneered the concept of a robotaxi purpose-built for ride-hailing and created groundbreaking innovations along the way, using NVIDIA technology.

“The world has never seen a robotics company like this before,” said Huang. “Zoox started out solely as a sustainable robotics company that delivers robots into the world as a fleet.”

Since 2014, Zoox has been on a mission to create fully autonomous, bidirectional vehicles purpose-built for ride-hailing services. This sets it apart in an industry largely focused on retrofitting existing cars with self-driving technology.

A decade later, the company is operating its robotaxi, powered by NVIDIA GPUs, on public roads.

Computing at the Core

Zoox robotaxis are, at their core, supercomputers on wheels. They’re built on multiple NVIDIA GPUs dedicated to processing the enormous amounts of data generated in real time by their sensors.

The sensor array includes cameras, lidar, radar, long-wave infrared sensors and microphones. The onboard computing system rapidly processes the raw sensor data collected and fuses it to provide a coherent understanding of the vehicle’s surroundings.

The processed data then flows through a perception engine and prediction module to planning and control systems, enabling the vehicle to navigate complex urban environments safely.

NVIDIA GPUs deliver the immense computing power required for the Zoox robotaxis’ autonomous capabilities and continuous learning from new experiences.

Using Simulation as a Virtual Proving Ground

Key to Zoox’s AV development process is its extensive use of simulation. The company uses NVIDIA GPUs and software tools to run a wide array of simulations, testing its autonomous systems in virtual environments before real-world deployment.

These simulations range from synthetic scenarios to replays of real-world scenarios created using data collected from test vehicles. Zoox uses retrofitted Toyota Highlanders equipped with the same sensor and compute packages as its robotaxis to gather driving data and validate its autonomous technology.

This data is then fed back into simulation environments, where it can be used to create countless variations and replays of scenarios and agent interactions.

Zoox also uses what it calls “adversarial simulations,” carefully crafted scenarios designed to test the limits of the autonomous systems and uncover potential edge cases.

The company’s comprehensive approach to simulation allows it to rapidly iterate and improve its autonomous driving software, bolstering AV safety and performance.

“We’ve been using NVIDIA hardware since the very start,” said Levinson. “It’s a huge part of our simulator, and we rely on NVIDIA GPUs in the vehicle to process everything around us in real time.”

A Neat Way to Seat

Zoox’s robotaxi, with its unique bidirectional design and carriage-style seating, is optimized for autonomous operation and passenger comfort, eliminating traditional concepts of a car’s “front” and “back” and providing equal comfort and safety for all occupants.

“I came to visit you when you were zero years old, and the vision was compelling,” Huang said, reflecting on Zoox’s evolution over the years. “The challenge was incredible. The technology, the talent — it is all world-class.”

Using NVIDIA GPUs and tools, Zoox is poised to redefine urban mobility, pioneering a future of safe, efficient and sustainable autonomous transportation for all.

From Testing Miles to Market Projections

As the AV industry gains momentum, recent projections highlight the potential for explosive growth in the robotaxi market. Guidehouse Insights forecasts over 5 million robotaxi deployments by 2030, with numbers expected to surge to almost 34 million by 2035.

The regulatory landscape reflects this progress, with 38 companies currently holding valid permits to test AVs with safety drivers in California. Zoox is currently one of only six companies permitted to test AVs without safety drivers in the state.

As the industry advances, Zoox has created a next-generation robotaxi by combining cutting-edge onboard computing with extensive simulation and development.

In the image at top, NVIDIA founder and CEO Jensen Huang stands with Zoox CEO Aicha Evans and Zoox cofounder and CTO Jesse Levinson in front of a Zoox robotaxi.

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From Materials Simulation to Experimental Astronomy, New NVIDIA AI Software Unlocks Scientific Discoveries

NVIDIA CUDA-X libraries, microservices and reference code accelerate AI for science.
June 22, 2026 by
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At the ISC conference running in Hamburg this week, NVIDIA is introducing new software that speeds AI for science, from chemistry and materials discovery to the search for dark matter. 

The NVIDIA DAQIRI library and new NVIDIA ALCHEMI NIM microservices — as well as the NVIDIA cuPhoton reference code, coming soon — turn work that once took hours or days on CPUs into real-time, GPU-accelerated pipelines. 

They’re a part of NVIDIA CUDA-X, a collection of tools and libraries that deliver dramatically higher performance across application domains, including AI and high-performance computing.

These performance gains are large and have real impact. Across disciplines, scientists are using AI and accelerated computing to generate data and insights with instruments and surveys faster than ever.  

For example, running on NVIDIA GB200 NVL72 systems, cuPhoton speeds loading, reading, processing and analysis of FITS data — the standard astronomical file format — from observatories and telescopes. In early access, cuPhoton accelerated loading and reading of FITS images collected by the Rubin Observatory’s Legacy Survey of Space and Time (LSST) by 14,900x. It also enabled up to 8,400x faster signal processing and analysis using 32 NVIDIA Grace Blackwell superchips. 

Ultimately, this means faster insights from the LSST camera — the largest digital camera ever built — which captures images of billions of far-away galaxies, as well as closer, faint objects that don’t reflect much light.

New Software, From the Lab Bench to the Telescope

The new software accelerates research on dark matter, materials simulation and more.

NVIDIA cuPhoton is a reference code for scientists looking to extract insights from multidimensional data collected from telescopes, X-rays and laser experiments. It’s built to load, process, analyze and visualize petabytes of data, and can be used alongside other NVIDIA CUDA-X technologies to build an end-to-end accelerated pipeline for work in fields including astrophysics and astronomy. 

Researchers at Princeton University collaborated with NVIDIA to develop cuPhoton and will use it — along with Harvard University — for processing and analysis of massive data collected from observatories and  dark energy surveys. 

NVIDIA DAQIRI — short for Data Acquisition for Integrated Real-time Instruments — is a high-performance networking library that streams data from fast detectors and sensors into NVIDIA software. Older systems are tied to fixed hardware and can drop data when instruments produce it faster than they can save it. DAQIRI keeps up by handling the stream as it arrives. 

A research project called A-GHOST was developed by scientists from CERN, the University of Chicago and University College London, in the framework of CERN openlab. It uses DAQIRI to run AI in real time on collision data recorded by the ATLAS Experiment at CERN. A-GHOST analyses data that  would normally be rejected by ATLAS  — over 99% of it, due to storage constraints — allowing it to catch potentially interesting signals that would otherwise be lost.

NVIDIA ALCHEMI comprises a collection of domain-specific microservices and a toolkit for accelerating chemical and materials discovery, with applications across battery materials, catalysts, OLED displays, beauty products and more. 

NVIDIA released in March two ALCHEMI NIM microservices for batched geometry relaxation (BGR) and batched molecular dynamics (BMD). These AI-accelerated tools let researchers simulate millions of molecules and materials at once: BGR to find their most stable structures, BMD to simulate how they move over time.

In addition, ALCHEMI is expected to soon include a microservice for the widely used Vienna Ab initio Simulation Package (VASP), enabling researchers to run materials simulations with higher GPU throughput. By running multiple VASP calculations on a single GPU with the NVIDIA Multi-Process Service, the microservice achieves a 3x speedup for geometry optimization — the process of finding the most stable arrangement of atoms in a material.

Plus, developers and researchers can use the ALCHEMI Toolkit to accelerate training of AI surrogate models called machine learning interatomic potentials and easily build custom, high-performance atomistic simulation workflows.

How Lila Sciences Runs the Scientific Method Nonstop With NVIDIA ALCHEMI 

Lila Sciences — which is building a scientific superintelligence platform and autonomous lab for life sciences, chemistry and materials science — collaborated with NVIDIA on a high-fidelity magnet simulation using ALCHEMI, demoed at NVIDIA GTC San Jose in March. 

Lila Sciences accelerated high-throughput materials screening by 50x using the ALCHEMI NIM microservice for BGR, identifying stable candidates that have higher chances of being synthesized. It then accelerated the calculation of magnetic properties by 30% for shortlisted candidates using the ALCHEMI VASP microservice in early access.

Lila Sciences conducts materials simulation with NVIDIA ALCHEMI. The image above, courtesy of Lila Sciences, depicts film coupons cut out from a sample synthesized in a sputterer, a system for creating ultrathin, highly uniform coatings of metals or ceramics onto a surface.

The speedups compound. ALCHEMI’s specialized kernels for TensorNet gave Lila a 6x speedup in training and inference and reduced memory usage by 3x, enabling simulations that previously took weeks in just days. 

Instead of running one experiment at a time, this approach evaluates multiple materials simultaneously in GPU memory and can be generalized for use cases spanning: 

  • Materials discovery — screening novel, stable compositions at scale 
  • Energy — discovering active, earth-abundant catalysts for producing chemicals and fuels
  • Electromagnetics — understanding and predicting complex magnetic behaviors

ALCHEMI sits at the simulation layer, generating the physical-science data that feeds the rest of the loop.

In addition, Lila Sciences accelerates scientific discovery with the full NVIDIA stack, using NVIDIA Megatron-LM and NVIDIA Nemotron for training — including the Nemotron 3 Nano and Nemotron 3 Super open models, as well as the NeMo RL and NeMo Gym libraries. The company also taps into NVIDIA BioNeMo for molecular generation, NVIDIA Triton and NIM microservices for inference serving, and NVIDIA Omniverse libraries for digital twins. 

“The work showcases using a powerful computing stack assembled to accelerate discovery at a scale no individual scientist could achieve alone,” said Andy Beam, cofounder and chief technology officer of Lila Sciences.

Availability

The NVIDIA ALCHEMI Toolkit and Toolkit-Ops are available for download from Github and PyPI. ALCHEMI NIM microservices are available for download from the NVIDIA NGC catalog. The ALCHEMI NIM microservice for VASP is expected to be available later this summer. 

DAQIRI is now available on GitHub. CuPhoton is expected to be available this summer.

Learn more about NVIDIA AI for science.

See notice regarding software product information. 

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Eco Wave Power Turns Waves Into Watts With NVIDIA AI Infrastructure and Digital Twins

June 22, 2026 by
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The next era of AI will not be defined by compute alone. Its growth will be determined by energy.

 As accelerated computing scales across AI factories, agentic AI, industrial AI, edge computing and physical AI — including robotics and autonomous systems — global electricity demand is rising at unprecedented speed. 

In many regions, expanding grid infrastructure to meet that need requires years of permitting, transmission upgrades, land acquisition and capital investment.

This challenge is reshaping how the world thinks about energy infrastructure for AI.

Eco Wave Power, a member of the NVIDIA Inception startup program’s Sustainable Futures initiative, is developing technology — powered by NVIDIA AI infrastructure and digital twins — that converts energy from ocean waves into clean electricity using existing marine infrastructure. By using already-built coastal structures, wave energy generation can be deployed closer to areas with growing power demand — including ports, industrial zones and future AI infrastructure hubs.

“Wave energy is one of the largest renewable energy sources that exists,” said Inna Braverman, cofounder and CEO of Eco Wave Power. “Everybody wants it, but nobody can do it, so I looked at the current problems with harnessing wave power and I asked: How do we simplify it?”

Turning the Sea Into a Power Source 

Harnessing Earth’s natural cycles for power generation isn’t a new concept. Wind and solar energy have been well established industries for decades. 

Waves are on the way to completing this trifecta of power-producing elements. 

In the U.S. alone, wave energy could produce over 60% of annual energy consumption, according to the Energy Information Administration. 

It all starts with floaters — noninvasive floating infrastructure attached to breakwaters or sea walls to capture the power generated by waves breaking against the shoreline. 

The density of seawater is roughly 800x the density of air, allowing larger amounts of energy to be generated using much smaller devices than wind turbines. 

The next step is managing and distributing that power. While previous companies faced a bottleneck at this stage — due to having their computer hardware in the floater, leading to potential damages during rough currents — Eco Wave Power puts its computers, sensors, hydraulic conversion and electric parts on land at centers, keeping expensive hardware dry and safe from storms. 

“Wave energy is the least intermittent source of renewable energy,” Braverman said. “Solar energy — for example — is great, but you have night, winter, cloud coverage and pollution that all impact production. With wave energy, you can generate around the clock.” 

AI Wave Energy Layer Using NVIDIA Omniverse Libraries and Accelerated Compute

As AI infrastructure expands, energy systems themselves are becoming increasingly intelligent.

Digital twins of wave patterns and floating infrastructure — built with NVIDIA Omniverse libraries — can simulate wave conditions, structural behavior, deployment configurations and operational scenarios before physical installation begins. These virtual environments can help optimize engineering decisions, reduce deployment risk and accelerate infrastructure planning.

At the operational layer, NVIDIA accelerated computing and AI technologies enable real-time optimization of wave energy systems through predictive analytics, anomaly detection, environmental forecasting and predictive maintenance. AI models can continuously analyze ocean conditions, equipment performance and energy generation patterns to improve efficiency and operational resilience.

AI can also orchestrate energy-aware computing infrastructure by aligning energy-intensive workloads with periods of stronger renewable generation and dynamically optimizing power utilization across distributed systems. 

Ocean Powered Data Centers on the Horizon 

Eco Wave Power operates projects in Jaffa Port, Israel, created in collaboration with EDF Power Solutions and the Israeli Energy Ministry, and in the Port of Los Angeles, developed in collaboration with AltaSea and Shell. Eco Wave Power is also developing new projects in Portugal at the Port of Leixões, Suao Port in Taiwan, and Mumbai, India, with Bharat Petroleum. 

Wave power has already demonstrated its ability to handle consumer energy needs — and is now showing potential to support data centers. 

“We have a possibility to link AI factories directly to wave energy, because a lot of data centers are moving toward the coast,” Braverman said. “They need cooling and water, so they’re now located in ports.” 

Pilots are already underway at the port of Los Angeles to showcase how wave energy can be the sole power source for a data center without tapping into the existing grid energy.

AI software serves as the control layer for this data center pilot, planning compute tasks based on the available power supply. For example, the software can monitor and predict when waves will be stronger throughout the week based on weather patterns — and accordingly allocate more intensive compute tasks for these periods. 

“We exist, we work, we’re grid connected and we have so much of this resource,” Braverman  said. “The energy is needed now, so I think we’re in the right place at the right time and we’re innovative, but we’re not futuristic, and that’s what sets us apart.” 

Explore how NVIDIA is driving the future of energy.

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