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Year: 2024

NVIDIA Advances Physical AI With Accelerated Robotics Simulation on AWS

NVIDIA Isaac Sim is now available on cloud instances of NVIDIA L40S GPUs in Amazon EC2 G6e instances, offering a 2x boost for scaling robotics simulation, and faster AI model training.
December 3, 2024 by
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Editor’s note: As of June 6, 2025, NVIDIA Edify is no longer available as an NVIDIA NIM microservice preview. To explore available visual AI models, visit build.nvidia.com.

Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes. Vention creates pretrained skills to ease development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.

These leading robotics startups are all making advances using NVIDIA Isaac Sim on Amazon Web Services. Isaac Sim is a reference application built on NVIDIA Omniverse for developers to simulate and test AI-driven robots in physically based virtual environments.

NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by NVIDIA L40S GPUs. And with NVIDIA OSMO, a cloud-native orchestration platform, developers can easily manage their complex robotics workflows across their AWS computing infrastructure.

This combination of NVIDIA-accelerated hardware and software — available on the cloud — allows teams of any size to scale their physical AI workflows.

Physical AI describes AI models that can understand and interact with the physical world. It embodies the next wave of autonomous machines and robots, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.

With physical AI, developers are embracing a three computer solution for training, simulation and inference to make breakthroughs.

Yet physical AI for robotics systems requires robust training datasets to achieve precision inference in deployment. Developing such datasets, however, and testing them in real situations can be impractical and costly.

Simulation offers an answer, as it can significantly accelerate the training, testing and deployment of AI-driven robots.

Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and Training

Simulation is used to verify, validate and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.

Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.

Using NVIDIA OSMO in the cloud allows teams to orchestrate and scale complex ‌robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.

Isaac Sim provides access to the latest robotics simulation capabilities and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.

Using a reference workflow that combines NVIDIA Omniverse Replicator, a framework for building custom synthetic data generation (SDG) pipelines and a core extension of Isaac Sim, with NVIDIA NIM microservices, developers can build generative AI-enabled SDG pipelines.

These include the USD Code NIM microservice for generating Python USD code and answering OpenUSD queries, and the USD Search NIM microservice for exploring OpenUSD assets using natural language or image inputs. The Edify 360 HDRi NIM microservice generates 360-degree environment maps, while the Edify 3D NIM microservice creates ready-to-edit 3D assets from text or image prompts. This eases the synthetic data generation process by reducing many tedious and manual steps, from asset creation to image augmentation, using the power of generative AI.

Rendered.ai’s synthetic data engineering platform integrated with Omniverse Replicator enables companies to generate synthetic data for computer vision models used in industries from security and intelligence to manufacturing and agriculture.

SoftServe, an IT consulting and digital services provider, uses Isaac Sim to generate synthetic data and validate robots used in vertical farming with Pfeifer & Langen, a leading European food producer.

Tata Consultancy Services is building custom synthetic data generation pipelines to power its Mobility AI suite to address automotive and autonomous use cases by simulating real-world scenarios. Its applications include defect detection, end-of-line quality inspection and hazard avoidance.

Learning to Be Robots in Simulation

While Isaac Sim enables developers to test and validate robots in physically accurate simulation, Isaac Lab, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on AWS Batch.

Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.

Several robotics developers are embracing NVIDIA Isaac on AWS to develop physical AI, such as:

  • Aescape’s robots are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.
  • Cobot has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.
  • Cohesive Robotics has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.
  • Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.
  • Standard Bots is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.
  • Swiss Mile is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.
  • Vention, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.

Learn more about Isaac Sim 4.2, now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs on AWS Marketplace.

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Coherent Breaks Ground on Expanded Texas Facility, Scaling AI’s Optical Backbone

Coherent’s expansion at its Sherman, Texas, campus scales what it calls the world’s first volume production 6-inch indium phosphide fab, a key supplier across NVIDIA’s AI stack.
June 16, 2026 by
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AI runs at the speed of light. More and more, that light is made in Texas.

Coherent broke ground today on an expanded manufacturing building in Sherman, Texas. 

The company makes the lasers, optical components and compound semiconductors that wire AI systems together — and runs what it calls the world’s first 6-inch indium phosphide fab. 

NVIDIA founder and CEO Jensen Huang and Coherent CEO Jim Anderson were on hand for the ceremony, joined by Sherman Mayor Shawn Temann and Adriana Cruz, executive director of Texas Economic Development and Tourism, who delivered remarks.  

The expanded building will scale production of the same InP wafers that carry data between chips, servers and data centers at the speed of light — the optical backbone of modern AI infrastructure.

It’s the kind of milestone that turns a commitment into construction: a concrete step in expanding advanced semiconductor manufacturing in the United States.

“AI is the ultimate general-purpose technology,” Huang said during a conversation with Anderson at the groundbreaking. “Because intelligence is fundamental — the ability to process information, to reason and solve problems — it affects every single industry.”

Public programs like the CHIPS Act, funded at roughly $50 billion, were designed to bring chip manufacturing back to the U.S. 

As part of today’s event, Coherent is announcing a $50 million CHIPS Act grant to help finance the expanded Sherman facility — building on roughly $17 million in earlier support from the Texas CHIPS program and the Sherman Economic Development Corporation.

NVIDIA’s own commitment to produce up to $500 billion of AI infrastructure in the U.S. through industry partnerships with new sites in Arizona and Texas adds private-sector momentum.

“Coherent is a world-class company, and the work you do is vital to our future, vital to the future of artificial intelligence and vital to reindustrializing the United States,” Huang said.

NVIDIA founder and CEO Jensen Huang and Coherent CEO Jim Anderson.

Compound semiconductors like indium phosphide and gallium arsenide — the materials behind the high-speed networking and optical interconnects that modern AI runs on — don’t get the headlines that logic chips do. But their domestic supply chains have been thin for years. Today’s event was an argument that the gap is closing.

When 576 GPUs span eight racks and operate as a single system — as they will in NVIDIA Vera Rubin Ultra NVL576, which links eight NVLink racks of 72 NVIDIA Rubin Ultra GPUs into one 576-GPU domain — copper can’t carry the signal across that distance. 

To connect hundreds of thousands of processors separated by hundreds or thousands of feet across a data center, the only way to solve that problem is silicon photonics, Huang explained. 

As signaling rates climb, the reach of a metal trace shrinks, and spanning eight racks in copper would burn power on retimers and signal conditioning that a data center would rather spend on compute. 

Optics pays a one-time penalty to move from electrical to light, but once paid, distance is nearly free. At NVL576 scale, light is the most power-efficient option. 

NVIDIA and Coherent aren’t new to each other — they’ve worked together for roughly two decades. 

In March, they deepened the relationship into a multiyear strategic partnership: NVIDIA is investing $2 billion in Coherent to support R&D, future capacity and U.S.-based manufacturing, alongside a multibillion-dollar purchase commitment for advanced laser and optical networking products.

Sherman, a city of roughly 45,000 people an hour north of Dallas, has become the latest dateline for the AI era — emblematic of a boom built as much on picks, shovels and manufacturing muscle as on software.

“When we get to full capacity, this site will support more than 550 direct jobs — and thousands of jobs, direct and indirect,” Anderson said.

What the factory ships isn’t a single product dropped into a single slot. It’s the lasers, transceivers and pluggable optical modules that move data across NVIDIA networking — each enabling a different part of the system.

“As AI systems grow larger and more powerful, connectivity is just as important as compute,” Anderson said. “AI runs on compute, but it scales on connectivity — and Sherman is where that connective tissue gets built.”

Today’s event made that visible.

Before the groundbreaking, guests toured the existing fab and previewed the equipment that will populate the expanded building once it’s running. An NVIDIA rack stood on the factory floor, one of the six stops on the tour. 

The tour was followed by a fireside chat with Huang and Anderson, where the two CEOs discussed the partnership and what scaling domestic optical manufacturing means for the AI buildout ahead.

“Today marks an important milestone — not just for Coherent, but for American manufacturing and for the future of AI infrastructure,” Anderson said. 

The semiconductor laser was born in U.S. labs — Bell Labs demonstrated a room-temperature version in 1970 — before the technology and its manufacturing largely migrated overseas.

“We were founded as a manufacturing company in 1971. We’ve always been a U.S. manufacturing company — and after 50 years, the most advanced 6-inch indium phosphide line in the world is right here in Sherman,” Anderson said. 

That manufacturing gap shows up in the wafers themselves: while silicon fabs run on 12-inch wafers, most of the world’s InP production is still stuck on 3- and 4-inch wafers — lower yields and far fewer components per run. 

Moving to 6-inch wafers roughly quadruples the usable area of a 3-inch wafer (area scales with the square of the diameter), driving down cost and unlocking the volume the AI buildout demands.


It took 50 years to build the first line, Huang said — and in one year, they’ve quadrupled it, a measure of the demand for accelerated computing.

Inside, the core processes are familiar: lithography, photoresist, depositing and etching materials, layer by layer. The difference is the material. On an InP substrate, engineers grow exotic compound-semiconductor layers and tune them for precise optical properties — the physics that lets a chip emit and modulate light.

Today, that InP travels inside Coherent’s pluggable optics — transceivers about the size of a USB stick that plug into the front of NVIDIA networking switches and move data between racks across the data center floor, where copper can’t reach. Each module carries an indium phosphide laser. 

Those same modules now help enable NVIDIA Spectrum-X Photonics and Quantum-X Photonics switches with co-packaged optics: Coherent supplies the external laser module that plugs into the switch’s front plate. 

And as NVIDIA works to keep optics from becoming the next bottleneck, demand for those lasers only climbs.

“Ten years from now, I think we’ll look back and realize AI is what made it possible to invest in sustainable energy, upgrade our energy grid and reconstitute a workforce,” Huang said. “You can’t have only information workers in an economy — you also have to have builders. We have an opportunity over the next 10 years to reshape our communities and be much more balanced.”

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How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies

A year after declaring itself an “AI maker, not an AI taker,” the UK is delivering sovereign compute, showcasing breakthrough startup and enterprise AI deployments across biology, agentic AI, coding and more.
June 7, 2026 by
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A year ago at London Tech Week, NVIDIA founder and CEO Jensen Huang and U.K. Prime Minister Keir Starmer made a declaration: the U.K. would be an AI maker, not an AI taker. 

At this year’s event, NVIDIA and its partners are showcasing how that commitment is producing real momentum across the nation’s infrastructure, startups and enterprises. 

U.K. technology leaders are innovating across healthcare and life sciences, coding, agentic AI, inference and more — all running on sovereign AI deployments.

“A year ago, we said the U.K. would be an AI maker, not an AI taker,” said U.K. AI Minister Kanishka Narayan. “Today we’re delivering on that — with sovereign compute powering British startups to push the boundaries of what AI can do, from drug discovery to healthcare to robotics. This is what it looks like when a country backs its own talent with the infrastructure to match. 

“NVIDIA’s decision to invest billions here is a reflection of the strength of what’s being built in Britain,” he added. “We are determined to make sure the next generation of AI breakthroughs happens in this country, and we have everything we need to make it happen.”

Commitment to Compute

Over the past year, the number of AI cloud providers planning to deploy AI infrastructure on U.K. soil has doubled. 

Nebius has announced plans to expand customers and cloud capabilities with three new deployments of advanced NVIDIA AI infrastructure, as the NVIDIA AI Cloud ecosystem partner continues to build out its commercial and AI R&D hub in London. Combined, the deployments are expected to reach 65 megawatts when fully ramped up in 2027.

CoreWeave is building in the U.K. Government’s AI Growth Zones, and seven more NVIDIA AI Cloud ecosystem partners have plans in the pipeline. BT and Nscale announced plans to build sovereign AI data centers across three existing BT sites in the U.K., combining NVIDIA AI infrastructure, Nscale’s full stack and BT’s trusted nationwide connectivity backbone. 

From Fund to Frontier

Central to that sovereign compute story is Isambard-AI — the U.K.’s most powerful computer. Built on 5,400 NVIDIA GH200 Grace Hopper Superchips and running entirely on zero-carbon electricity, it’s the engine behind some of the U.K.’s most ambitious AI research. 

The U.K. government’s Sovereign AI Fund is putting that capability to work by backing homegrown companies and providing the domestic infrastructure needed to scale their ambitions. 

Among its first recipients is Ineffable Intelligence, which recently announced a collaboration with NVIDIA to build the future of reinforcement learning infrastructure. 

Other recipients include four U.K.-based NVIDIA Inception startups, each pushing the AI frontier using Isambard-AI. These startups are:

Cosine Builds Sovereign Coding Platform

Cosine is building an end-to-end sovereign AI coding platform for highly regulated industries such as financial services, critical infrastructure and national security. Using Isambard, Cosine is training a new, large-parameter, mixture-of-experts, multimodal agentic LLM for natively handling data types beyond text and image. 

“Access to Isambard enables the project, full stop,” said Alistair Pullen, cofounder and CEO of Cosine. “We already have the people who know how to do this. We have the data. We have the infrastructure and the training. The thing we’ve never had is this level of compute.”

Cursive Trains Self-Improving AI Systems

Cursive is building self-improving AI systems that learn continuously from real-world data, enabling them to operate autonomously over long periods of time. This is unlocked through new memory-augmented architectures with dramatically larger context windows, currently in development using the Sovereign AI Fund resources. In addition, the team recently adopted the NVIDIA Megatron-LM framework for distributed training at scale.

“The Sovereign AI Fund is more than just processing power — it’s a statement about investing in AI in the U.K.,” said Talfan Evans, cofounder and CEO of Cursive. “Sovereignty is actually now a buying criterion — and it’s a challenge to tap into the resources we uniquely have as U.K. and European companies.”

Doubleword Optimizes Inference to Deliver Abundant Intelligence Tokens

Doubleword, the U.K.’s first dedicated inference lab, optimizes every layer of the AI stack to maximize what it calls “IQ per dollar.” The company deploys open models including NVIDIA Nemotron 3 Super 120B and builds on the NVIDIA Dynamo inference framework. 

On Isambard, Doubleword’s early results achieved 70x faster model cold starts — aka model loading times — and 4x lossless KV cache compression, critical advancements for long-running agentic workloads. The result: inference at 90-95% lower costs than other leading inference providers.

Image courtesy of Doubleword.

“Sovereign AI is most impactful at the inference layer,” said Meryem Arik, cofounder and CEO of Doubleword. “Inference is when you’re actually getting the value from the model — we want that value created in the U.K., with U.K. compute and U.K. data centers.”

Prima Mente Uses Foundation Models to Study Alzheimer’s and More

Prima Mente builds biological foundation models to identify new biomarkers, subtypes and drug targets of Alzheimer’s, Parkinson’s and ALS. With its Isambard allocation, the company is developing Pleiades 2, a foundation model combining five biological data modalities. 

Achieving nearly 3x speedups in model training with NVIDIA Blackwell GPUs, Prima Mente also uses NVIDIA Parabricks for genomic data processing and NVIDIA Transformer Engine for model optimization.

“Research shows Alzheimer’s might be 25 different subgroups of disease, and we want to help by using AI to identify these subtypes and the biology within the cells as they change,” said Hannah Madan, cofounder of Prima Mente.

Video courtesy of Nebius and Prima Mente.

AI Talent, Policy and Production

NVIDIA’s £2 billion investment in the U.K. startup ecosystem — in collaboration with leading venture capital firms — is bringing new capital and advanced AI infrastructure to major U.K. hubs including London, Oxford, Cambridge and Manchester. 

U.K. membership in the NVIDIA Inception program has increased by 50% over the past year. AI-native companies like Doubleword, Synthesia and PolyAI are scaling globally from U.K. roots. 

At last year’s London Tech Week, NVIDIA announced a collaboration with the U.K Department for Science, Innovation and Technology on 6G and AI skills. The 6G collaboration has seeded testbeds at four U.K. universities. In May, the NVIDIA Deep Learning Institute (DLI) delivered two new courses — added to support the nation’s wireless research community — to participants from over 30 U.K. universities.

Plus, as part of this AI skills collaboration, NVIDIA DLI courses are offered as part of QA’s AI Apprenticeships in England. 

And the NVIDIA Developer Program now includes more than 200,000 U.K. developers. 

The Sovereign AI Forum, which launched last year with seven charter members, convened the country’s AI leadership to turn policy into deployment roadmaps. Over the past year, the Forum has welcomed dozens of participants across government, industry and the startup community — turning policy into deployment roadmaps.

And enterprise AI is moving from pilot to production:

  • Apian is building digital twins of two National Health Service hospitals, combining autonomous devices, ground robots, computer vision and robotic simulation.
  • Deliverance AI is helping regulated enterprises to run, govern and scale AI agents inside their own environment — through a single control plane. The Agentic Operating System is built for organizations where data sovereignty is non-negotiable.
  • Glass Futures has installed an AI-driven digital twin of its glass furnace capable of testing and predicting new, optimal ways to make glass. The digital twin taps into NVIDIA accelerated computing and the NVIDIA PhysicsNeMo framework.
  • OneAdvanced is fine-tuning NVIDIA Nemotron 2 Nano 9B with the NeMo AutoModel for its AI-consultation and triage app with sovereign, real world NHS Primary Care patient triage data.
  • Orbital Industries has announced codesigned, NVIDIA Vera Rubin DSX AI Factory-compliant AI infrastructure that accelerates time to first token.
  • Reading Football Club is partnering with Stelia to establish an AI Centre of Excellence, combining Stelia’s full-stack AI platform with accelerated compute infrastructure from NVIDIA and Lenovo.

It all reflects momentous progress in U.K. AI leadership — and offers a glimpse of where it’s heading.

Join NVIDIA at London Tech Week.

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Industrial Software Leaders Build Secure, Autonomous AI Engineers With NVIDIA NemoClaw

Showcased at GTC Taipei at COMPUTEX, autonomous AI engineers compress weeks of simulation work into just hours.
June 2, 2026 by
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Accelerated computing has revolutionized industrial engineering, compressing simulation times from weeks to hours. 

Today’s remaining challenges sit in the end-to-end workflow surrounding the simulations: computer-aided design, meshing, simulation setup and debugging, as well as post-processing and generating summary reports of these processes. 

At GTC Taipei at COMPUTEX, NVIDIA and more than a dozen engineering software providers are showcasing how autonomous AI agents automate this entire workflow.

These AI engineers are based on NVIDIA NemoClaw, an open blueprint for building specialized, long-running agents with a secure runtime and frontier models. 

NemoClaw includes a choice of harness — meaning it can be integrated with various orchestration frameworks enterprises use to deploy and coordinate agents, such as OpenClaw and Hermes — as well as a model router and NVIDIA NeMo libraries for customization. 

Users can easily deploy NemoClaw from NVIDIA DGX Spark personal AI supercomputers, as well as through enterprise data centers and cloud service providers. NVIDIA OpenShell — the open source runtime at its core — governs how each agent accesses files, networks and tools, enforcing policy-based security at every layer.

Industrial Engineering Leaders Build AI Agents Across Design, Engineering, Simulation

Industrial software leaders are building AI engineers for computer-aided engineering (CAE) and electronic design automation (EDA) use cases across automotive, aerospace, semiconductors and manufacturing.

Cadence is building an autonomous register-transfer level (RTL) engineer with NemoClaw that orchestrates Cadence Design Systems ChipStack for design and verification. The workflow was featured yesterday in a GTC Taipei keynote demo and is cutting time for RTL verification — a key step in digital circuit design — from weeks to hours.

Dassault Systèmes is actively productizing the 3DEXPERIENCE Agentic Platform to operate long-running and autonomous agents for design, simulation and manufacturing operations, in a secured environment powered by NVIDIA NemoClaw and OpenShell.  

Siemens is integrating NVIDIA NemoClaw and OpenShell into Fuse EDA AI Agent, a purpose-built autonomous agent that plans and orchestrates domain-scoped multi-tool workflows across semiconductor, 3D integrated circuit and printed circuit board system design.

Synopsys is collaborating with NVIDIA to apply agents to end-to-end engineering workflows with NVIDIA NemoClaw. Ansys Icepak, part of the Synopsys portfolio, is being demoed on the COMPUTEX show floor this week, used within a NemoClaw-based autonomous AI engineer to mesh, simulate and optimize GPU electronics cooling designs.

Image courtesy of Synopsys.

Startups Extend the Reach of Agentic AI

In addition, cutting-edge startups are building AI engineers for their workflows — all using NVIDIA NemoClaw.

Flexcompute is applying OpenShell to its Tidy3D and PhotonForge agents for multiphysics co-packaged optics design. Flexcompute’s autonomous AI workflow combines optical, electrical and thermal simulation to explore thousands of design variants overnight, producing higher-performing components with lower energy consumption. NVIDIA is using Flexcompute technology for the design and optimization of advanced optical and photonic devices.

 

Video courtesy of Flexcompute.

Luminary is building a long-running AI engineer using NemoClaw to dramatically reduce the time and complexity of training AI physics models by autonomously orchestrating data generation, machine learning model selection, and training and re-training loops.

 

Video courtesy of Luminary.

Neural Concept is deploying an agent for electric motor design. The workflow chains electromagnetic, structural and noise, vibration and harness simulations in a multistep engineering pipeline. Watch the full demo.

 

Video courtesy of Neural Concept.

nTop, the geometry engine behind JetZero’s blended-wing-body aircraft program, is using NVIDIA NemoClaw to run autonomous design workflows that compress days of geometry iteration into hours.

 

Video courtesy of nTop.

PhysicsX is partnering with the Microsoft Surface team to build an electronics thermal simulation agent that compresses weeks of manual CAE workflows into automated, AI-driven design cycles. Bringing together the PhysicsX platform, Microsoft Discovery and NVIDIA NemoClaw, the agent automates the full thermal simulation lifecycle for consumer devices such as Microsoft Surface laptops — from mesh sensitivity analysis and simulation data generation, through physics AI model training and optimization-loop execution, to continuous accuracy monitoring across the design exploration process.

 

Video courtesy of PhysicsX.

P-1 AI is building Archie, an AI mechanical and electrical engineer that already works with data center cooling and critical power systems, and will soon work for automotive, aerospace and national security use cases. In a workflow representative of its work with Daikin Applied Americas, Archie synthesizes requirements, selects components, runs design trade studies and produces engineering artifacts to help industrial manufacturers scale engineering capacity.

 

Video courtesy of P-1 AI.

SimScale is adopting NVIDIA NemoClaw to build autonomous simulation agents for hundreds of cross-industry engineering use cases, including noise, vibration and harshness analysis, automating workflows that previously required multiple engineers working over several weeks. 

 

Video courtesy of SimScale.

Synera is building an engineering agent for injection molding — a manufacturing process used to efficiently mass-produce identical parts by injecting molten material, usually plastic, into a custom mold — with Autodesk Moldflow, NVIDIA OpenShell with OpenClaw, as well as Nemotron models.

 

Video courtesy of Synera.

Learn more about NVIDIA technologies for CAE and watch NVIDIA founder and CEO Jensen Huang’s GTC Taipei keynote in replay.

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