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How AI and Machine Learning Accelerate Product Development Workflows in Manufacturing

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From recommendation tools built into ecommerce sites and streaming platforms to sophisticated image editing in smartphones, AI and machine learning applications have rapidly advanced over the last few years.  

Across the manufacturing and product design industry, companies are experimenting with powerful AI solutions for many use cases and workflows.   

Learn more about the latest developments in AI for manufacturing at NVIDIA GTC, running March 21-24. 

According to a 2020 MIT Technology Review Insights survey, manufacturing is one of the top two sectors adopting AI. Al and machine learning bring many benefits to manufacturing use cases, including: 

  • Product research, development and production 
  • Inventory management 
  • Process and quality control 
  • Predictive maintenance 

Leading companies are already integrating advanced AI solutions into their workflows. For example, Foxconn Group has implemented AI for automated high-precision inspection of its products’ components and tools using NVIDIA software libraries and the NVIDIA EGX platform for accelerated computing.  

A recent survey conducted by Peerless Research Group, including responses from over 300 product designers, engineers, researchers and other professionals in aerospace, automotive and industrial machinery, has identified Al and simulation as the two key technologies that will have the biggest impact on product design and development over the next five years.  

Enhanced Workflows for Design and Engineering 

When it comes to applying AI in product development, generative design is a common use case, as the organic shapes generated by computer-aided design and engineering tools with this capability are striking compared to conventional designs.  

Image courtesy of ANSYS.

The use of AI and machine learning in CAE is increasing, allowing engineers and analysts to: 

  • Gain near-real-time insights for design exploration – similar to how ANSYS Discovery, with its NVDIA CUDA-based GPU-accelerated solver, has reduced simulation time from days to minutes. 
  • Better manage time-consuming simulation tasks such as geometry preparation, meshing, management of the result data and identification of trends and anomalies in the vast amount of post-processing data. 
  • Use data from previous simulations to train machine learning models to narrow the design space and identify the key design parameters.  

Monolith AI, a member of the NVIDIA Inception program designed to support the global startup ecosystem, has helped manufacturing companies optimize research and development processes by reducing the numbers of simulations, tests and prototypes. This enables companies to deliver faster and better products by applying machine learning to data generated during the engineering design process.  

A New AI Framework for Physics 

The need for physics-driven AI models is growing fast – especially in industries like energy, climate science and life sciences. With a framework like NVIDIA Modulus, manufacturers and design engineers can create physics-driven AI models and unleash new capabilities in industrial simulation.  

NVIDIA Modulus is a neural network framework that blends the power of physics and partial differential equations with AI to build more robust models for better analysis. Modulus trains neural networks to learn from data and use the laws of physics to model the behavior of complex systems. The surrogate model can then be used in various applications, from industrial use cases to climate science. Once a model is trained, Modulus can do the inference in near real time or interactively. 

NVIDIA Modulus.

With Modulus, professionals in manufacturing and product development can explore different configurations and scenarios of a model by changing its parameters, allowing them to gain deeper insights about the system or product.  

Digital Twins That Go Beyond Simulations 

Digital twin technology is also increasingly being adopted across manufacturing and product development. Using NVIDIA Omniverse Enterprise, a virtual world simulation and collaboration platform for 3D workflows, designers and engineers can develop and operate physically accurate digital twins that support a wide range of AI-enabled use cases. 

With AI and digital twins, companies can better predict and optimize operational performance, resulting in faster production times, enhanced efficiency and improved products or processes. 

Explore AI Further at GTC 

Hear from NVIDIA partners and customers to get insights on how they’re using AI in manufacturing workflows. Featured GTC sessions include: 

  • Using AI in Engineering Simulation [S41563]  
  • New Era of Digital Twins With Omniverse [SE2644]  
  • Get Started With AI for Engineering Simulations Using NVIDIA Modulus on Rescale Platform [S42087]
  • Breakout Session: Building Industrial Digital Twins on Omniverse [SE2311] 
  • Toward a Synchronized Manufacturing Digital Twin: NVIDIA Omniverse Has the Building Blocks [S41632] 
  • A Vision of the Metaverse: How Will We Build Connected Virtual Worlds [S42114] 

Check out additional manufacturing and product development sessions at GTC. Watch the keynote address by NVIDIA founder and CEO Jensen Huang, on March 22 at 8 a.m. Pacific, to hear the latest news on NVIDIA technologies.  

Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care

Children’s Hospital of Philadelphia is using open source AI tools to model children’s hearts in seconds — with the goal of enabling safer, more precise care for kids with congenital heart disease.
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Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX

Perplexity’s local agent platform now supports Windows RTX PCs, giving more users a way to run private AI workflows without cloud credits.
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As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device.

Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information across files and handle recurring work. Sensitive information stays on device, and locally completed work doesn’t consume Perplexity Computer credits. Users can also orchestrate work up to cloud models for more advanced research and reasoning.

Today, Perplexity is adding Portable Computer in the Perplexity app for Windows on compatible NVIDIA GeForce RTX PCs and NVIDIA RTX PRO Workstations, bringing powerful agentic AI to more Windows PC users. The release builds on existing support for NVIDIA DGX Spark systems and RTX PCs running Linux.

Local Agents Powered by NVIDIA RTX

Perplexity brings local and cloud AI together in one app, letting users work with sensitive files on their PCs and take advantage of Computer’s built-in tools such as the built-in browser and proprietary SPACE sandbox.

For tasks that call for more advanced reasoning, Portable Computer can also identify when a task needs cloud support, asking the user for permission before sending information off-device.

The app simplifies setup with a local model, such as Qwen 3.8 27B, that is post-trained to work with Perplexity Computer and optimized for NVIDIA RTX GPUs. Users can put the agent to work without having to research models or configure the complex software stack typically required to run local AI.

Connectors for Microsoft Outlook, OneDrive, Word, Google Drive, Gmail, Slack and GitHub extend that experience across the files and apps already part of users’ daily workflows.

For example, the agent can help with:

  • Engineering: Review open pull requests in a connected GitHub project, organize them by status and identify next steps. Computer can also flag outdated documentation and submit proposed updates for review.
  • Finance: Point Computer at two years of brokerage summaries, consolidated 1099s and tax returns, and have it trace the recurring holdings creating the most avoidable fees and tax drag, with every figure cited to the exact file and page — all without a document ever reaching a chatbot.
  • Startups: Ask Computer why activation went flat, and the agent analyzes the funnel export locally to find where new signups drop off between install and first completed task, then posts the top insights straight to the team’s Slack channel.

Try Portable Computer on Windows PCs Today

Portable Computer is available for NVIDIA GeForce RTX and RTX PRO GPUs with 24GB or more of VRAM. NVIDIA DGX Station support is expected to come soon. 

Try Perplexity Portable Computer today.

#ICYMI: The Latest Updates From NVIDIA Local AI

🧠Z.ai’s GLM 5.3 Flash provides impressive performance and visual intelligence at low cost, optimized for DGX Station and dual DGX Spark systems.

🐋DeepSeek-v4.1 Flash significantly reduces key-value cache memory demands and operating costs for complex AI agent workloads, delivering remarkable intelligence per dollar.

⚡Qwen has released Qwen3.8-Flash-Next, an open-weight multimodal mixture-of-experts model, and an early preview of Qwen4, which can run locally on a single DGX Spark with NVFP4 and punches well above its weight.

👾GLM 5.3 is Z.ai’s 744-billion-parameter flagship model, tuned for agent sessions that run for hours on DGX Station and a cluster of four DGX Spark systems. 

Follow NVIDIA RTX Spark on X, Instagram, TikTok and Facebook — and stay informed by subscribing to the NVIDIA Local AI newsletter. Follow NVIDIA Workstation on LinkedIn and X

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NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC

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At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers delivering insights.

Read on to learn more about what NVIDIA’s highlighting at the show.


NVIDIA AI for Media Brings Real-Time Intelligence to Broadcast, Sports and Production Workflows 🔗

Media companies are increasingly integrating AI into live production, sports, news and streaming workflows to unlock richer performance insights, verify video authenticity, and enhance and localize content — all without disrupting trusted broadcast environments.

At IBC 2026 in Amsterdam, NVIDIA announced a major expansion to NVIDIA AI for Media — a collection of GPU-accelerated software development kits (SDKs), NVIDIA NIM microservices, playbooks, and blueprints that enhance audio, video and augmented-reality effects for media and entertainment workflows — to unlock new ways to understand motion, verify and enhance video, localize programming and build AI-powered media applications. 

The NVIDIA Synthetic Video Detector (SVD) NIM microservice, announced earlier this year at SIGGRAPH, helps organizations assess the probability of whether footage is authentic or AI-generated, giving editorial, content-authentication, digital-forensics and media-integrity teams another point of analysis in their review process.

Since its initial release, SVD’s accuracy has reached 99.3% for text-to-video content and 97.7% for image-to-video content, with especially large gains on difficult image-to-video cases. 

Dalet is integrating SVD into a secure, cloud-hosted verification workflow for news organizations. This allows editorial teams to submit footage through SVD, inspect and review the resulting scores and metadata within a Dalet interface. 

TwelveLabs announced the general availability of Compliance by TwelveLabs, its first application built on the company’s video intelligence platform, helping media and broadcast teams rapidly screen content against regional and custom compliance standards. The solution integrates SVD to add frame-level authenticity signals and confidence scores, enabling media teams to identify potentially synthetic media within the same compliance workflow.

Wowza, whose Wowza Streaming Engine media server technology powers more than 35,000 video deployments across over 170 countries, will distribute SVD through the Wowza Video Intelligence Framework. The solution, powered by NVIDIA-accelerated infrastructure, will enable broadcasters, streaming providers and other organizations to analyze live video feeds and extract data around detected objects, scenes and signs of AI generation in real time. It can be deployed and run on premises, at the edge, in the cloud, across hybrid deployments or fully air-gapped, giving organizations greater control over critical media workflows.

NVIDIA 3D Body Pose estimates 2D and 3D human joint locations and angles from video captured by a single camera, helping turn motion into structured data without marker-based capture systems.

For sports organizations, that data can support player and athlete movement tracking, biomechanics and performance analysis, replay enhancement, officiating and adjudication workflows, player-safety applications, and virtual interaction and immersive experiences. 

The technology can also provide structured human-motion data for content-creation workflows. When mapped to a compatible character rig, joint and motion data can serve as input for animation blocking, digital doubles, character retargeting and virtual-production experiences.

Vizrt is using Body Pose technology in live virtual-studio environments, with tracked body movement driving real-time 3D lighting effects such as reflections, shadows and environmental rendering.

Video Frame Generation (VFG) makes video motion appear smoother by using generative AI to create new frames between the original frames of a video. It can increase frame rates by 2x or 4x while preserving visual quality and temporal consistency, enabling more fluid sports, slow-motion replays, live media and other high-motion video experiences. VFG also supports frame-rate conversion and frame boosting for generative AI video workflows.

Ross Video is integrating VFG into its Rio Replay platform to create AI-assisted slow-motion video for sports production.

The work supports 6x slow-motion generation for sports replay. Development is underway toward 8x interpolation, meaning generated intermediate frames can give replay teams smoother motion without requiring every frame to be captured by an ultrahigh-frame-rate source camera. 

NVIDIA Video Super Resolution (VSR) uses AI to upscale video while reducing noise, blur and compression artifacts. New streaming modes let developers choose between real-time performance and higher image quality, while adjustable controls help achieve the desired level of enhancement. VSR also adds 10-bit video support and improves overall performance and quality. VSR is available through the NVIDIA Video Effects SDK and a NIM microservice for use in streaming, broadcast, conferencing, video playback and content-creation applications. 

The technology can support video players, conferencing applications, creator tools, streaming services, transcoders and broadcast systems through a common interface.

NVIDIA TrueHDR converts standard-dynamic-range video into high-dynamic-range output in real time, reaching up to approximately 2,000 nits while preserving local contrast and adapting brightness to the content.

VSR, VFG and TrueHDR can be combined within a single video-effects pipeline — helping media companies enhance existing content libraries for streaming, transcoding, gaming and creator workflows.  

The NVIDIA LipSync and Active Speaker Detection NIM microservices help developers build localization systems for interviews, news, sports, entertainment and other programming where multiple people may appear on screen.

LipSync transforms mouth movement in an input video to match a target audio track while preserving natural head pose, blinking and body movement. The new release improves facial occlusion handling and better preserves teeth, lip and facial textures.

The new Active Speaker Detection NIM microservice no longer requires speaker diarization for multiple audio tracks, adds voice activity detection and expands NIM microservice deployment support through a gRPC interface and broader GPU compatibility.

NDI is using NVIDIA AI for Media, including the NVIDIA LipSync NIM microservice, to enable real-time translation, lip-synced dubbing and regional language adaptation within existing broadcast workflows. By generating multiple language experiences from a common media stream, the approach can help broadcasters reach global audiences while reducing the bandwidth, infrastructure and production complexity traditionally required for multilingual distribution.

Studio Voice includes new Microphone Profiles built on NVIDIA Studio Voice NIM microservices, giving users more control over the tonal character of enhanced speech.

The capability is designed to suppress background noise, reduce room reverberation and improve speech clarity, then shape the enhanced output into a selected microphone profile for more polished live communications, streaming, podcasting and content creation.

Try NVIDIA AI for Media NIM microservices. See the latest NVIDIA and partner workflows at IBC 2026.


NVIDIA Holoscan for Media Provides Open Media Exchange Layer to Build and Connect Live Media Applications 🔗

As broadcasters, streaming services and sports organizations adopt software and AI, the infrastructure behind live content is becoming more flexible, more connected and increasingly built on shared accelerated computing.

The integration of Media Exchange Layer (MXL) with NVIDIA Holoscan for Media accelerates that transition — providing the common exchange layer that helps media applications connect and operate together. 

Holoscan for Media is an open reference architecture and developer toolkit for building AI-powered media functions and applications for software-defined live production. MXL adds an open way for those software-based media functions to exchange live video, audio and data across a distributed environment. 

As production functions move into software, developers can build applications that share accelerated infrastructure, connect dynamically and evolve independently. That can help media companies use infrastructure more efficiently, introduce new capabilities faster and reduce the amount of custom integration required between applications.

The integration also creates a stronger foundation for AI in live media. AI processing, video applications and traditional media functions can increasingly operate on the same accelerated infrastructure and in the same software-defined environment.

For technology vendors, this expands the opportunity to build applications that can work across broader, multi-vendor ecosystems. For media companies, it creates a path toward infrastructure that can adapt as formats, applications and AI capabilities change.

See the demo at IBC in EBU Stand 10.D21. ​Learn more about Holoscan for Media.


NVIDIA Sports Intelligence Playbooks Chart a Path to Multimodal AI for Sports 🔗

Sports is becoming a proving ground for a broader shift in AI: from general-purpose models toward fine-tuned open models built on proprietary data.

NVIDIA Sports Intelligence Playbooks are designed to accelerate that transition. They give leagues, media companies and technology providers structured frameworks to fine-tune NVIDIA open models on their own sports footage and annotations, creating multimodal AI that can understand the rules, players, scoring, strategy and context unique to a sport.

Sports organizations hold large volumes of proprietary video, metadata and performance information that are difficult for competitors to replicate. The playbooks provide a practical blueprint for converting those assets into AI capabilities that can underpin new analytics products, media experiences, automation tools and revenue streams.

The playbooks span the AI lifecycle, including data preparation, fine-tuning, inference, evaluation, optimization and deployment, and bring together NVIDIA technologies including Nemotron, NeMo AutoModel, Megatron Bridge, NIM microservices and NVIDIA accelerated computing

By providing an integrated path from model customization to production, Sports Intelligence Playbooks can reduce the cost and complexity of building specialized sports AI while increasing demand across its compute, software and inference stack.

Early testing demonstrates the potential of domain specialization. When evaluated on previously unseen footage using question formats similar to those used in training,  multiple-choice accuracy increased from approximately 53% to 94% and open-ended evaluation from approximately 5.7% to 66%.

Machina Sports is integrating Sports Intelligence Playbooks with its sports-native data, evaluation and agent infrastructure, enabling rights holders to turn proprietary media and expertise into private, deployable intelligence for live production, content and fan experiences.

The opportunity also expands as agentic AI becomes increasingly adopted. With the NVIDIA AI-Q Blueprint, organizations can use their domain-specific sports models as expert intelligence within agents that reason across video, enterprise data and software systems, extending the playbook from sports understanding into decision-making and automation.

Wowza is integrating vision language models, including NVIDIA Cosmos 3 and Nemotron, into the Wowza Video Intelligence Framework, fine-tuned through NVIDIA Sports Intelligence Playbooks to detect sports-specific moments in live streams and reduce time to action. 

Explore NVIDIA Sports Intelligence Playbooks.


NVIDIA Brings Multilingual Content Localization to Live Broadcast 🔗

Reaching global audiences with live programming requires more than translating words. Language nuances, voice, timing, facial movement, captions and onscreen graphics must work together in real time, while preserving the editorial intent and production quality of the original program.

To help broadcasters, sports leagues, rights holders and streaming services bring these elements into a unified, software-defined, real-time localization workflow, NVIDIA is bringing its Content Localization technologies to the NVIDIA Holoscan for Media developer toolkit. Designed for broadcast and streaming developers, the reference workflow enables captions, translated audio, dubbing, synchronized video and localized graphics.

Content Localization with Holoscan for Media provides a reference for how localization technologies can work together in software-defined broadcast applications. Developers can select the capabilities needed for each program, market or distribution channel rather than deploying separate infrastructure for every localized version.

Content Localization with Holoscan for Media incorporates the latest advancements from NVIDIA AI for Media, including improved LipSync when faces are partially obscured and enhanced Active Speaker Detection to help applications identify who’s speaking in multi-person scenes.

Expanding the Reach of Live Programming

Localization can transform the reach and economics of live programming. A shared, composable workflow can help media companies introduce regional coverage faster, serve more audiences and tailor experiences for individual markets — while preserving the timing, visual context and editorial control required for live production.

Technologies from AI-Media, CAMB.AI, Chyron and Panjaya each address a specific part of content localization with Holoscan for Media, from adapting voice and onscreen delivery to creating multilingual captions and translated audio, localizing graphics, and preserving expression and identity across live and on-demand content.

The Content Localization technologies also support file-based, streaming and post-production applications. Developers can use application programming interfaces for on-demand workflows and the Holoscan for Media reference workflow when localization must run as part of a live media environment. Together, they provide a consistent foundation for building multilingual media services across production and distribution.

Learn more about NVIDIA Holoscan for Media and AI for Media.