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Day: March 2, 2022

Beyond Be-leaf: Immersive 3D Experience Transports Audiences to Natural Worlds With Augmented Reality

Factory 42 uses NVIDIA RTX, CloudXR and 5G to power an interactive Green Planet AR Experience.
March 2, 2022 by
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Imagine walking through the bustling streets of London’s Piccadilly Circus, when suddenly you’re in a tropical rainforest, surrounded by vibrant flowers and dancing butterflies.

That’s what audiences will see in the virtual world of The Green Planet AR Experience, an interactive, augmented reality experience that blends physical and digital worlds to connect people with nature.

During the Green Planet AR Experience, powered by EE 5G, visitors are led through a living rainforest and six distinct biomes by a 3D hologram of Sir David Attenborough, familiar to many as the narrator of some of the world’s most-watched nature documentaries.

All images courtesy of Factory 42.

Audiences engage and interact with the plant life by using a mobile device, which acts as a window into the natural world.

To bring these virtual worlds to life in a sustainable way, award-winning studio Factory 42 combined captivating storytelling with cutting-edge technology. Using NVIDIA RTX and CloudXR, the creative team elevated the AR experience and delivered high-fidelity, photorealistic virtual environments over a 5G network.


Natural, Immersive AR Over 5G — It’s a Stream Come True

The Green Planet AR Experience’s mission is to inspire, educate and motivate visitors toward positive change by showcasing how plants are vital to all life on earth. Through the project, Factory 42 and the BBC help audiences gain a deeper understanding of ecosystems, the importance of biodiversity and what it means to protect our planet.

To create an immersive environment that captured the rich, vivid colors and details of natural worlds, the Factory 42 team needed high-quality imagery and graphics power. Using mobile edge computing allowed them to deliver the interactive experience to a large number of users over EE’s private 5G network.

The AR experience runs on a custom, on-premises GPU edge-rendering stack powered by NVIDIA RTX 8000 professional GPUs. Using NVIDIA RTX, Factory 42 created ultra-high-quality 3D digital assets, environments, interactions and visual effects that made the natural elements look as realistic as possible.

With the help of U.K.-based integrator The GRID Factory, the GPU edge-rendering stack is connected to EE’s private 5G network using the latest Ericsson Industry Connect solution for a dedicated wireless cellular network. Using NVIDIA RTX Virtual Workstation (RTX vWS) on VMware Horizon, and NVIDIA’s advanced CloudXR streaming solution, Factory 42 can stream all the content from the edge of the private 5G network to the Samsung S21 mobile handsets used by each visitor.

“NVIDIA RTX vWS and CloudXR were a step ahead of the competitive products — their robustness, ability to fractionalize the GPU, and high-quality delivery of streamed XR content were key features that allowed us to create our Green Planet AR Experience as a group experience to thousands of users,” said Stephen Stewart, CTO at Factory 42.

The creative team at Factory 42 designed the content in the AR environment, which is rendered in real time with the Unity game engine. The 3D hologram of Sir David was created using volumetric capture technology provided by Dimension Studios. Spatial audio provides a surround-sound setup, which guides people through the virtual environment as digital plants and animals react to the presence of visitors in the space.

Combining these technologies, Factory42 created a new level of immersive experience — one only made possible through 5G networks.

“NVIDIA RTX and CloudXR are fundamental to our ability to deliver this 5G mobile edge compute experience,” said Stewart. “The RTX 8000 GPU provided the graphics power and the NVENC support required to deploy into an edge rendering cluster. And with CloudXR, we could create robust connections to mobile handsets.”

Sustainability was considered at every level of construction and operation. The materials used in building The Green Planet AR Experience will be reused or recycled after the event to promote circularity. And combining NVIDIA RTX and CloudXR with 5G, Factory 42 can give audiences interactive experiences with hundreds of different trees, plants and creatures inside an eco-friendly, virtual space.

Experience the Future of Streaming at GTC

Learn more about how NVIDIA is helping companies create unforgettable immersive experiences at GTC, which runs from March 21-24.

Registration is free. Sign up to hear from leading companies and professionals across industries, including Factory 42, as they share insights about the future of AR, VR and other extended reality applications.

And watch the keynote address by NVIDIA CEO Jensen Huang, on March 22 at 8 a.m. Pacific, to hear the latest news on NVIDIA technologies.

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NVIDIA Vera Rubin Maximizes Intelligence per Dollar for Post-Training Workloads — a Key Metric for Agentic AI

Lowest cost per token from extreme codesign maximizes intelligence per dollar for post-training in the agentic era.
July 17, 2026 by
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NVIDIA Vera Rubin Maximizes Intelligence per Dollar for Post-Training Workloads — a Key Metric for Agentic AI - Image 1

Think of a professional athlete. What separates elite performers is what happens between games: continuous refinement, adjusting to new opponents and sharpening skills based on what the last game exposed.

Agentic AI works the same way. A model is no longer asked for an answer. It’s given a goal and has to keep adapting as environments shift, edge cases emerge and tools change. Unlike a generative model responding to a prompt, an agentic model must plan, use different tools and recover from problems it encounters mid-run.

That’s why post-training, the phase that refines a model after initial training on raw data, is no longer a one-time finishing step. It’s continuous, because the environment that agentic models operate in shifts fast. The tools an agent uses can change week to week. Edge cases surface in production that no test set anticipated. Each deployment brings its own codebase, policies and environment.

Post-training runs loop back from production as new problems surface. The compute footprint grows not because any single run is larger, but because the runs never stop. Agentic AI introduces a new compute pattern for post-training, making it the central workload of the agentic era and the primary driver of intelligence per dollar.

The goal of post-training is to maximize intelligence per dollar by maximizing the yield of every forward and backward pass in the continuous learning cycle. The forward pass — inference — is measured in cost per token. That means that every improvement to cost per token flows directly into intelligence per dollar. 

Agentic Post-Training Demystified

Post-training is where intelligence is built. In pretraining, the model learns to predict the next token, which gives it fluency but not intelligence. Post-training is where it learns to write code, plan a multistep task, use a search tool and recover when something goes wrong. Inference is what comes after: the model working on the job, priced in cost per token.

Because there’s no answer key to memorize, only a reward, the model learns by reinforcement learning (RL) techniques. When given a task, it writes out an attempt — the forward pass — the same work it does on the job. The attempt is scored, and the lesson updates the model’s weights — the backward pass. Across millions of attempts, intelligence grows.

Each step is compute intensive, and running this loop at scale is an orchestration problem: thousands of environments generating rollouts in parallel, rewards being verified and updated weights flowing back into training with accelerators fully utilized. NVIDIA NeMo open libraries, such as NeMo Gym for training environments and NeMo RL for distributed post-training, turn post-training from bespoke research code into repeatable infrastructure. 

Why Intelligence per Dollar Extends Cost per Token 

If inference is the revenue engine, post-training is the multiplier: the more capable the model, the higher the value of every token served. 

Cost per token is the key metric for the inference factory: the all-in cost of delivering 1 million tokens. Intelligence per dollar sits one layer up, answering a different question: what does it cost to build a model worth serving, and keep it worth serving as its environment changes?

The two are nested, not competing. AI infrastructure that lowers cost per token also lowers the cost of every point of intelligence built into the model. And every point of intelligence built in raises the value of every token the inference factory serves. 

In other words, cost per token measures operating yield; intelligence per dollar measures whether the investment in model intelligence is paying off. 

Maximizing Intelligence per Dollar: Post-Training Nemotron 3 Ultra

NVIDIA Nemotron 3 Ultra — an open weight, 550-billion-parameter mixture-of-experts (MoE) model, offers verifiable benchmarks and a fully disclosed post-training recipe run on NeMo RL. It scored 71.7% on a standard real-world coding benchmark, SWE-bench verified, where it produced a working fix for roughly seven in 10 real software bugs from open source projects, each one checked against the project’s own tests.  

Illustrative 20 billion rollout tokens, based on prior-generation Nemotron 3 Super’s ~1.2 million rollouts at ~10,000 tokens each, scaled up for the larger Ultra model. Intelligence per dollar between platforms is independent of this assumption; the absolute values scale with the token count.

The NVIDIA Blackwell platform lowers cost per run and makes the frequent post-training the agentic era demands economically viable. That intelligence is reaped across every token served.

The NVIDIA Vera Rubin platform extends the trajectory further, training the largest models with one-fourth the GPUs of the Blackwell generation. It was codesigned from end to end to maximize intelligence per dollar for the agentic post-training load: more rollouts per run, more environments in play and post-training cycles that never stop.

Post-Training Workflows in Action

Prime Intellect’s Lab continuously post-trains frontier open models on NVIDIA Blackwell and uses NVIDIA Dynamo for inference orchestration. With Vera Rubin, Prime Intellect plans to scale reinforcement learning environments, generate more rollouts per run and accelerate training-to-inference iteration loops to maximize intelligence per dollar for businesses.

Prime Intellect has optimized its sandbox infrastructure to integrate with NVIDIA Vera CPUs, enabling low-latency, energy-efficient reinforcement learning. Open source tools and models such as NVIDIA Nemotron and NVIDIA NeMo Gym are also integrated into its software stack. When comparing realistic RL sandbox workloads against alternative x86 architectures, Prime Intellect found that Vera delivers, on average, 30% greater throughput per CPU.

Perplexity’s RL post-training stack runs asynchronously across hundreds of NVIDIA GPUs, with an RDMA-based weight transfer engine that syncs trillion-parameter models in under two seconds between training and inference compute nodes. The resulting post-trained Qwen3 235B models are then served on NVIDIA GB200 NVL72 systems.

Together AI provides post-training as a service, including supervised fine-tuning, RL and direct preference optimization. The service is delivered via a feature-rich application programming interface and software development kit that supports the full range of post-training on its AI Native Cloud platform. It has been running on NVIDIA’s platform and optimized kernel libraries, and is looking to harness the Vera Rubin platform next.

Learn more about NVIDIA Vera Rubin, the platform for AI factories to maximize intelligence per dollar across workloads. And explore NVIDIA’s full-stack platform for training frontier models.

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